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arXiv 2024年

作者： He, Yuanpeng Li, Lijiang Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China Department of Computer and Information Science University of Macau China

Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation, which often fails to solve the problem related to credibility completely. Therefore, based on the framework of evidential deep learning, this paper integrates the evidential predictive results in the cross-region of mixed and original samples to reallocate the confidence degree and uncertainty measure of each voxel, which is realized by emphasizing uncertain information of probability assignments fusion rule of traditional evidence theory. Furthermore, we design a voxel-level asymptotic learning strategy by introducing information entropy to combine with the fused uncertainty measure to estimate voxel prediction more precisely. The model will gradually pay attention to the prediction results with high uncertainty in the learning process, to learn the features that are difficult to master. The experimental results on LA, Pancreas-CT, ACDC and TBAD datasets demonstrate the superior performance of our proposed method in comparison with the existing state of the arts. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Enhancing Mobile Robot Speed Control: PID Controller Optimization with Bio-Inspired Algorithms 4

Enhancing Mobile Robot Speed Control: PID Controller Optimiz...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Singh, Sangeeta Uma, H.R. Bhargav, H.K. Arya, Sujata Singh, Praveen Natrayan, L. JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201 301 India BGS Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Karnataka Mandya571 448 India Basaveshwar Engineering College Department of Computer Science and Engineering Karnataka Bagalkot587 102 India Faculty of Law Viman Nagar Maharashtra Pune411 014 India Graphic Era Deemed to be University Department of Management Studies Uttarakhand Dehradun248002 India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

This research study presents an investigation into the optimization of PID controllers using bio-inspired algorithms for controlling the speed of mobile robots. The study begins by identifying a mathematical model that accurately represents the dynamics of the DC motor used in robots. Three different tuning methods, namely Ziegler-Nichols (ZN), Flower Pollination Algorithm (FPA), and Fish Swarm Optimization (FSO) are then utilized to design the PID controllers. The primary objective is to enhance the speed control performance of mobile robots. To evaluate the effectiveness of the optimization methods, the set speed is given to each of the three controllers, and their performance is compared. The results demonstrate that the optimization techniques outperform the conventional ZN method in achieving the desired speed control. Among the two bio-inspired algorithms, FPA yields exceptional outcomes in terms of time characteristics. The comparison of time characteristics serves as a benchmark for evaluating the performance of the controllers. It reveals that bio-inspired optimization methods, especially FPA, significantly improve the speed control of mobile robots. The time characteristics, such as rise time, settling time, overshoot, peak time, and peak value, illustrate the superior performance achieved through the optimization process. © 2024 IEEE.

关键词： DC motors

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Pandemic Bonds Issued by the Chinese Government Supported Post-Disaster Recovery from COVID-19 Pandemic

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Journal of Social Computing 2022年第2期3卷 158-170页

作者： Feng Liu Deli Kong Jinhua Kong Shiying Lu Zilong Xiao Qing Xu Aimin Zhou Jiayin Qi the Institute of AI for Education and also with the School of Computer Science and TechnologyEast China Normal UniversityShanghai 200062China the School of Business and Management Shanghai International Studies UniversityShanghai 201620China the Institute of Artificial Intelligence and Change Management Shanghai University of International Business and EconomicsShanghai 200336China the School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China the Department of Economics and Management Changsha University of Science and Technology410000 ChangshaChina the Department of Data Science and Computer Science Sun Yat-sen UniversityGuangzhou 510080China the Department of Software Beijing University of Posts and TelecommunicationsBeijing 100876China

During the SARS-CoV-2(COIVD-19)outbreak,China repeatedly stressed that the response to the pandemic required action at all levels of government,including the issuance of Pandemic Bonds to help the country return to work and ***,studies on the effectiveness of Pandemic Bonds during that period are *** with China’s national financial bond market data after COVID-19 in 2020,this paper focuses on the correlation between the Credit Spreads of the relevant bonds and the corresponding bond market rate of return,based on the Copula *** empirical analysis is also carried out for multiple dimensional groupings such as enterprises,industries,provinces,and bond *** results show that there is a significant positive correlation between the Credit Spreads of Pandemic Bonds and market *** addition,the market correlation is higher for Pandemic Bonds issued in Hubei Province,which is at the center of the 2020 pandemic,and the shorter the maturity of the Pandemic Bond issued,the stronger the relationship with market ***,this paper provides recommendations for financial regulators and policy makers to consider in their decisions on how to build a more resilient financial system under heavy economic,fiscal,and social pressures.

关键词： COVID-19 Pandemic Bond Credit Spread Copula model

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Design of Trading Mechanism and Blockchain-Based Spectrum Administration System for Space-Air-Ground Integrated Networks 2

Design of Trading Mechanism and Blockchain-Based Spectrum Ad...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Suri, Sahil Chhabra, Gunjan Bhuvaneswari, S. Babitha, B.S. Mali, Manisha P. Balaji, N.V. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India School of Mangement - UG Management Karnataka Bangalore India Engg. Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350388916

Complete connectivity and high-quality service are provided via a space-air-ground integrated network (SAGIN). Because it brings together devices from several untrusted authorities, an integrated network like this raises security risks. While blockchain's decentralized ledger features promise to address SAGIN's security flaws, they also pose the risk of incurring intolerable latency due to sequential request validation and consuming vast amounts of storage space required to maintain a full copy of the chain on each node in a large-scale networks. In order to facilitate the required inter-segment cooperation, it becomes conceivable to use several parallel blockchains for the effective management of various SAGIN with blockchain collaboration technology segments. To that aim, we present SAGIN's collaborative blockchain architecture as a means to an end - the efficient and safe administration of varied resources. While blockchain cooperation is possible, each section of SAGIN - the air, ground, and space networks - maintains its own specifically constructed blockchain. In SAGIN, spectrum management has become crucial as the number of wireless gadgets and services continues to skyrocket. Reliable spectrum sensing under lower signal-to-noise ratio (SNR) is still a major barrier for spectrum management, despite the fact that efforts to provide dynamic spectrum access are showing promise in promoting more spectrum sharing. To enhance the performance of spectrum sensing, a deep neural network-based detection structure is constructed to extract features data-drivenly from the received signal's covariance matrix. This approach is based on the idea that artificial intelligence (AI) can provide intelligence to wireless networks through learning and data mining. A method for blindly defining thresholds without using the system's past knowledge is suggested in order to eradicate the effect of noise uncertainty. The suggested scheme's detection effectiveness is enhanced in a low S

关键词： Quality of service

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Exploring Algorithmic Paradigms in Message Classification: Insights from the Enron E-mail Dataset 1

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Saeejil, Noor Ali Younis, Hussain A. Hayder, Israa M. Muthmainnah Apriani, Eka Aditi Sharma, M. Pd. Al Yakin, Ahmad Department of Management Information Systems College of Administration and Economics University of Basrah Basrah Iraq College of Education for Women University of Basrah Basrah61004 Iraq School of Computer Science University Sains Malaysia Gelugor11800 Malaysia Department of Computer Systems Techniques Qurna Technical Institute Southern Technical University Basrah61016 Iraq Universitas Al Asyariah Mandar Barat Sulawesi Indonesia IAIN Curup Curup Indonesia Pune India

ISBN: (数字)9789819761036

ISBN: (纸本)9789819761029

This research focuses on message classification, specifically distinguishing between legitimate and spam messages. The paper emphasizes the importance of preprocessing textual data using vectorizers, introducing Count Vectorizer and TF-IDF Vectorizer for this purpose. These vectorizers convert text into numerical representations. Along with libraries such as scikit-learn and nltk, is used for model implementation, providing machine learning and natural language processing capabilities, each initialized with specific parameters for optimization. Data is sourced from the Enron e-mail dataset on Kaggle, comprising around 500,000 e-mail linked to Enron’s investigation by the Federal Energy Regulatory Commission. The research objectives include training models with selected algorithms to accurately categorize messages and assessing their performance using metrics. Findings reveal weak positive correlations between message characteristics and the target variable. The developed models show promising performance, emphasizing the need to consider diverse factors and techniques in message classification. The study contributes insights into the relationships between message characteristics and classification accuracy, aiding the development of effective models across various domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Random forests

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Dynamic Identity-Based Cryptography using RSA 15

Dynamic Identity-Based Cryptography using RSA

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15th International Conference on Computing Communication and Networking technologies, ICCCNT 2024

作者： Krishnan, Gopala Karnik, M.P. Hemelatha, S. Nanda, Simranjeet Allirani, P. Kansal, Amit School of Mangement-PG Management Karnataka Bangalore India Vishwakarma Institute of Technology Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Quantum University Research Center Quantum University India

ISBN: (纸本)9798350370249

Dynamic identification-based cryptography is a rising shape of cryptographic era that makes use of best an identity in place of a pair of public and personal keys to encrypt facts. This technology is based totally at the RSA cryptosystem and makes use of the identity of a person to generate an ephemeral public key for each verbal exchange session. By using utilizing the identity of the person, dynamic identity-based totally cryptography can decrease the quantity of computational resources had to authenticate and encrypt messages for the duration of communiqué. Furthermore, this technology lets in for more potent encryption on the grounds that a person's identification changes over time so that the public key ought to be re-generated for every communiqué consultation. This method of encryption has the ability for use in more than one program, which includes mystery communication between government agencies and people, monetary applications and e-trade transactions. © 2024 IEEE.

关键词： Agencies Applications Includes Monetary Mystery Transactions

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

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Procedia computer science 2025年 258卷 893-902页

作者： Santosh Kumar Henge Avinash Bhagat Sanjeev Kumar Mandal Nikhil Reddy Viraati Ravleen Singh K. Rushi Kedhar Associate Professor Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal 506371 India Associate Professor School of Computer Applications Lovely Professional University Phagwara Punjab India Assistant Professor Department of CS and IT Jain (Deemed-to-be University) Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States of America Assistant Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddewaram AP India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach; it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate.

关键词： Fuzzy Logic Control (FLC) Artificial Neural Network (ANN) Multiple Layered Perceptron (MLP) Convolutional Neural Network (CNN) Feed Forward Neural Network (FNN) Optical Character Recognition (OCR) Handwritten (HW)

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Multilingual Lexical Simplification via Zero-Shot Paraphrasing

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2200-2214页

作者： Kang Liu Jipeng Qiang Yun Li Yi Zhu Yunhao Yuan Kaixun Hua Xindong Wu Department of Computer Science Yangzhou University Yangzhou China Department of Industrial and Management Systems Engineering University of South Florida Tampa USA Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei China

Lexical simplification (LS) method based on pretrained language models is a straightforward yet powerful approach for generating potential substitutes for a complex word through analysis of its contextual surroundings. Nonetheless, these methods necessitate distinct pretrained models tailored to diverse languages, often overlooking the imperative task of preserving a sentence’s meaning. In this paper, we propose a novel multilingual LS method via zero-shot paraphrasing (LSPG), as paraphrases provide diversity in word selection while preserving the sentence’s meaning. We regard paraphrasing as a zero-shot translation task within multilingual neural machine translation that supports hundreds of languages. Once the input sentence is channeled into the paraphrasing, we embark on the generation of the substitutes. This endeavor is underpinned by a pioneering decoding strategy that concentrates exclusively on the lexical modifications of the complex word. To utilize the strong capabilities of large language models (LLM), we further introduce a novel approach PromLS that incorporates the results of LSPG to generate heuristic-enhanced context, enabling the LLM to generate diverse candidate substitutions. Experimental results demonstrate that LSPG surpasses BERT-based methods and zero-shot GPT3-based methods significantly in English, Spanish, and Portuguese. We also demonstrate a substantial improvement achieved by PromLS compared to the previous state-of-the-art LLM approach. LS approaches usually assume that complex words and their replacements are individual terms, concentrating on word-for-word substitutions. To tackle the more challenging task of multi-word lexical simplification, including phrase-to-phrase replacements, we extend LSPG and PromLS into MultiLSPG and MultiPromLS. MultiLSPG identifies multi-word expressions matched with their corresponding word counts in specific positions, while MultiPromLS, akin to PromLS, utilizes these candidates to generate a heuristi

关键词： Multilingual Decoding Translation Rhetoric Large language models Training Neural machine translation Encoding Speech processing Probability distribution

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Accurate Brain Tumor Detection Using Laplacian Based GAN Model

Accurate Brain Tumor Detection Using Laplacian Based GAN Mod...

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Disruptive technologies (ICDT), International Conference on

作者： Mayank Raj Arun Kumar Sachin Bansal Neeru Singh Megha Jain Sreenu Banoth Department of Information Technology Jss Academy of Technical Education Noida Uttar Pradesh India Department of Computer Science Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of Computer Sceince DataSceince Jss Academy of Technical Education Noida Uttar Pradesh India School of Computer Science and Engineering IILM Univeristy Greater Noida Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The very complexity of the brain and the processes it operates with makes its tumors one of the most complicated malignancies that are difficult to detect and heal. Both benign and malignant kinds of tumors could seriously impair ordinary brain function. Improved results entail early, accurate diagnoses but traditional methods of biopsies and X-rays have disadvantages and risks. Improvements in deep learning (DL) and machine learning (ML) have helped diagnose brain tumors using non-invasive methods like magnetic resonance imaging (MRI). This paper applies Graph Neural Networks (GNN) and Laplacian-based image processing to propose an automated brain tumor recognition system depend on the MRI data from the BraTS 2020 dataset to classify brain lesions as benign or malignant. Before segmenting the images with a U-Net-based model to delineate brain lesions, the technique pre-processed MRI images using Laplacian filtering for better edge identification and noise reduction. Data imbalance was handled with a hybrid Laplacian-based U-Net GAN model, and a Fast Graph Convolutional Network (GCN) was applied to identify malignant cells. To improve generalization and avoid overfitting, dropout layers and data augmentation were applied. The model succeeded in identifying the tumor with a 90% accuracy on 1000 datasets, but when it was increased to 1500 datasets, the overfitting caused the result to decrease to 75% and stabilize at 70%. Although the issue is still being faced with bigger datasets, the findings show promise for early timely detection of tumors.

关键词： Accuracy Laplace equations Graph convolutional networks Magnetic resonance imaging Brain tumors Brain modeling Biomedical image processing Lesions Overfitting Cancer

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A Systematic Literature Review on Faculty Learning in Service-Learning 14

A Systematic Literature Review on Faculty Learning in Servic...

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14th IEEE Global Engineering education Conference, EDUCON 2023

作者： Bandi, Surendra Reddy Joshi, Gopalkrishna Shettar, Ashok Kandakatla, Rohit Hyderabad Institute of Technology and Management Department of Computer Science & Engineering Hyderabad India KLE Technological University Centre for Engineering Education Research Hubballi India KLE Technological University Hubballi India KG Reddy College of Engineering and Technology Department of Electronics and Communication Hyderabad India

ISBN: (纸本)9798350399431

Service-learning is a pedagogical approach which makes learning more meaningful. Researchers have reported this so far taking the cases of student learning experiences and impact studies. There are also studies which analyzed the role of community and academic institutions in the success of Service-learning. Faculty being an important stakeholder, less research took place on the role of faculty. When it is targeted to undergraduate students, Service-learning courses require to be designed and delivered in a distinctive style. Undergraduate students think and learn independently compared to children under 18 years of age. There are learning theories which are applicable for faculty and students. Faculty learning is equally important to make learning more effective. Faculty becoming co-learners while teaching increases the confidence of students in the learning process. As per the literature available, faculty learning as a specific area is not given focus in the process as compared to the other areas of service-learning like models of integrating service-learning into curriculum, benefits of service-learning, complementing professional skills and improved ethics etc. Hence this study is an attempt to explore the concept of faculty learning in service-learning and report the significance of faculty learning in implementing service-learning courses to undergraduate engineering students. © 2023 IEEE.

关键词： Students

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