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Blockchain-Based Communicator with Multicast Transmission 8

Blockchain-Based Communicator with Multicast Transmission

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nair, Praanesh Balakrishnan Varun Adhitya, G.B. Vineetha, K.V. Ullas, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

This paper examines the conceptualization and implementation of an instant messaging application, which is decentralized. The proposed work utilizes a blockchain with end-to-end encryption and digital signatures. The need for nodes dedicated to mining has been done away with, by introducing a new consensus mechanism called Proof of Message (PoM). The implementation demonstrates improved security over traditional centralized messaging systems while maintaining usability for small to medium-sized groups (50-100 users). When compared to conventional instant messaging applications which are centralized, the increase in latency has been found to be minimal, according to performance analysis. © 2024 IEEE.

关键词： Instant messaging

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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QCVNET - Leveraging Quantum Technology in Convolutional Neural Networks for Improved Cardiovascular Disease Prediction 5

QCVNET - Leveraging Quantum Technology in Convolutional Neur...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Karthigeyan, S. Diviya, M. Bhuvaneswari, R. Sabhanayagam, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Galgotias University UP Greater Noida India

ISBN: (纸本)9798331528539

The primary issue of global health today is Cardio-vascular diseases (CVDs) and thus requires accurate predictive models for detecting it early. Quantum technology combined with regular deep learning techniques are used by researchers here to improve the accuracy of forecast on heart diseases. While using an expanded dataset with clinical and demographic records, an innovative approach was found to use Quantum Convolutional Neural Network (QCVNET) structure that merged quantum layers into classical CNN framework with Principal Component Analysis (PCA) that allowed for consistency of features with respect to the quantum bits' number. The performance of the QCVNET was evaluated by comparing it to Quantum LSTM, Quantum DNN, Quantum GRU, and Quantum RNN, which are other quantum models enhanced with conventional neural networks, and other traditional non-quantum models like SVM, RF, RNN and DNN, using different indicators of quality like f1-score or recall. In early CVD prediction, the QCVNET model produced an impressive accuracy of 91.73%, beating all other models, showcasing the potential impact of quantum-enhanced deep learning for timely diagnosis and personalized treatment strategies while providing means to reduce the global burden of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Quantitative Susceptibility Mapping With Reduced Training Time Based on Local Field Transformation

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第2期9卷 1527-1538页

作者： Wu, Ming-Long Huang, De-Rong Cheng, Jhih-Shan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan National Cheng Kung University Institute of Medical Informatics Tainan701 Taiwan

A quantitative susceptibility mapping (QSM) approach using single-orientation imaging data is proposed in this study. The proposed method generates local field maps at five predefined orientations via deep learning from single-orientation local field maps, followed by physical dipole inversion using the analytical Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) algorithm. We termed the method pseudo COSMOS (pCOSMOS). By isolating the physical dipole inversion process from deep learning, training of the neural network model becomes greatly accelerated. Brain imaging data from 10 healthy subjects (7 training and 3 testing) acquired at 3T with five different orientations relative to the main magnetic field were included to evaluate the reconstruction quality. Results compared against two iterative (Morphology Enabled Dipole Inversion or MEDI, and Structural Feature-based Collaborative Reconstruction or SFCR) and two deep learning QSM approaches (QSMnet and Learned Proximal Convolutional Neural Network or LPCNN) using COSMOS as the reference standard revealed that the susceptibility maps obtained from pCOSMOS yielded global quantitative performance comparable to QSMnet and LPCNN, while being prominently superior to MEDI and SFCR. Estimation errors in magnetic susceptibility were between 0.01 and 0.02 ppm voxel-wise, and 0.006 ± 0.004 ppm for five selected gray matter areas. Visual inspection further suggested better preserved tissue boundaries in pCOSMOS than QSMnet and LPCNN. Training time was reduced from 2 days for LPCNN and 12 hours for QSMnet to 3.2 hours for pCOSMOS. It is concluded that the proposed pCOSMOS method is an alternative deep learning QSM approach when computational efficiency is essential. © 2017 IEEE.

关键词： Brain mapping

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Computational efficiency in sports talent identification – a systematic review

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International Journal of Applied Decision Sciences 2023年第3期16卷 358-384页

作者： Khan, Naveed Jeelani Ahamad, Gulfam Naseem, Mohd Sohail, Shahab Saquib Department of Computer Sciences Baba Ghulam Shah Badshah University Rajouri India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

The selection of talent for sports has always been of great concern. The research interest in the domain of computational decision-making for sports talent identification is on an increasing curve. The conventional approaches are being modelled into the scientific models using various analytical and mathematical computational techniques. This paper reviews some of the talent identification models and aims to project the current perspective of the computational techniques being employed in sports talent identification (TiD). Articles from a timeframe of 1995–2020 were systematically selected in accordance with the PRISMA guidelines. We remain focused on the computational methodology being employed in the TiD models. The review delivers the findings and highlights some of the inherent issues that are not being addressed by the existing TiD models. © 2023 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Sports

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Arora, Yojna Gupta, Neha Rathore, Yogesh Singh Bansal, Nidhi Mehta, Aina Chadha, Shikha Kumar, Ashwani Sharda University Department of Computer Science and Engineering Greater Noida India Symbiosis University of Applied Sciences School of Computer Science & Information Technology Madhya Pradesh Indore India Sharda University School of Engineering and Technology Department of Computer Science and Applications Uttar Pradesh Greater Noida India School of Engineering and Technology Department of Computer Science & Engineering Haryana Faridabad India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331508685

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end. © 2025 IEEE.

关键词： Adversarial machine learning

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A Conceptual Framework for a Critical Approach to the Digital World: Integrating Digital Humanities and Informal Learning into Educational Design 1

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3rd International Conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Georgopoulou, Maria-Sofia Troussas, Christos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Aigaleo Greece

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

Technology has opened up new possibilities in the way we live, communicate, and learn. The daily engagement with activities of the digital world and the re-conceptualization of citizenship highlights the need to connect school to the wider socio-cultural context, in order to prepare tomorrow’s citizens to thrive in the local and global community. To this end, this paper introduces a conceptual framework, which consists of three main pillars: technology as a subject of critical analysis by the humanities, intentional formal and informal learning, and learning theories. The novelty of this study lies in the nature of the educational design, which is oriented towards shaping active, literate, and autonomous identities in the digital world through authentic student-centered learning practices that reflect current pedagogical approaches. It addresses secondary education, as little work has been undertaken in this area, regarding Digital Humanities and connection of formal and informal learning spaces. By providing a basis for transforming educational practice into an active, engaging, critical, and autonomous learning experience, the proposed framework has the potential to revolutionize the way we approach the digital world in education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Disentangling Prosody Representations With Unsupervised Speech Reconstruction

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

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A Decentralized Communication Framework Based on Dual-Level Recurrence for Multiagent Reinforcement Learning

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IEEE Transactions on Cognitive and Developmental Systems 2024年第2期16卷 640-649页

作者： Li, Xuesi Li, Jingchen Shi, Haobin Hwang, Kao-Shing Northwestern Polytechnical University School of Computer Science and Engineering Xi'an710129 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804 Taiwan

Designing communication channels for multiagent is a feasible method to conduct decentralized learning, especially in partially observable environments or large-scale multiagent systems. In this work, a communication model with dual-level recurrence is developed to provide a more efficient communication mechanism for the multiagent reinforcement learning field. The communications are conducted by a gated-attention-based recurrent network, in which the historical states are taken into account and regarded as the second-level recurrence. We separate communication messages from memories in the recurrent model so that the proposed communication flow can adapt changeable communication objects in the case of limited communication, and the communication results are fair to every agent. We provide a sufficient discussion about our method in both partially observable and fully observable environments. The results of several experiments suggest our method outperforms the existing decentralized communication frameworks and the corresponding centralized training method. © 2016 IEEE.

关键词： Decision making

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BERT-based keyword extraction model for the Turkish language

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Neural Computing and Applications 2025年第16期37卷 9807-9819页

作者： Babayigit, Bilal Sattuf, Hamza Department of Computer Engineering Erciyes University Kayseri Turkey Graduate School of Natural and Applied Sciences Erciyes University Kayseri Turkey

Keyword extraction is crucial in natural language processing (NLP) tasks, aiding in information retrieval, document summarization, and content categorization. While many studies have discussed keyword extraction for different languages, the Turkish language presents unique challenges due to its rich morphology, complex syntax, and agglutinative nature. This paper proposes a keyword extraction model for Turkish based on the deep learning model of bidirectional encoder representation transformers (BERT) and NLP. The proposed model has been trained using a novel Turkish dataset specifically collected for this task. The dataset was fetched from over 128,000 theses published in the National Thesis Center of Türkiye. 90% of the dataset used for training the model, and 10% of the dataset used for testing. Our experimental results indicate that the proposed model outperforms similar existing methods highlighting a significant advancement in Turkish text keyword extraction. The performance of the proposed model achieved values of 97.77% F1-score, 97.84% precision, and 97.71% recall. © The Author(s) 2025.

关键词： Deep learning

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