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Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

computer Systems science & engineering 2023年第8期46卷 1981-1997页

作者： Pham Huy Thong Florentin Smarandache Phung The Huan Tran Manh Tuan Tran Thi Ngan Vu Duc Thai Nguyen Long Giang Le Hoang Son Graduate University of Science and Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam Institute of Information Technology Vietnam Academy of Science and TechnologyHanoi100000Vietnam VNU Information Technology Institute Vietnam National UniversityHanoi100000Vietnam Department of Mathematics University of New MexicoGallup87301New MexicoUSA University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen250000Vietnam Faculty of Computer Science and Engineering Thuyloi UniversityHanoi100000Vietnam

Clustering is a crucial method for deciphering data structure and producing new *** to its significance in revealing fundamental connections between the human brain and events,it is essential to utilize clustering for cognitive *** with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering *** data can lead to incorrect object recognition and *** research aims to innovate a novel clustering approach,named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering(PNTS3FCM),to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set(PFS)and Neutrosophic Set(NS).Our contribution is to propose a new optimization model with four essential components:clustering,outlier removal,safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled *** effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods,standard Picture fuzzy clustering(FC-PFS)and Confidence-weighted safe semi-supervised clustering(CS3FCM)on benchmark UCI *** experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.

关键词： Safe semi-supervised fuzzy clustering picture fuzzy set neutrosophic set data partition with noises fuzzy clustering

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An Exploration of Contagion Infection in Citrus Plant with Augmented Data using ConvNet 2

An Exploration of Contagion Infection in Citrus Plant with A...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Shanmugapriya, S. Dhanasekaran, S. Murugan, B.S. Kalasalinagam Academy of Research and Education Departement of Computer Science and Engineering India Kalasalinagam Academy of Research and Education Departement of Information Technology India M. Kumarasamy College of Eengineering Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350325799

Citrus is widely cultivated and consumed fruit across the global. It gains its popularity from its nutritional benefits. Citrus fruit has gained its international trading due to freshly consumed, processed products, oil production and other by-products. Citrus production is cut short mainly due its diseases. Identifying the diseases at early stage and stepping forward to cure them is vital role in improving the production. Automated disease identification cuts down the manual effort and also improves the efficiency in identification. With various degrees of accuracy, both machine learning and deep learning algorithms have been successful in identifying citrus leaf and fruit diseases. This research study proposes a novel disease classification method using CNN and also provides a comparative study with models like SVM, k-means clustering, ANN and CNN for disease identification. © 2023 IEEE.

关键词： Citrus fruits

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Accident Prediction Using KNN Algorithm 4

Accident Prediction Using KNN Algorithm

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4th International Conference on Emerging Research in Electronics, computer science and technology, ICERECT 2022

作者： Akhiladevi, M. Anitha, K. Amrutha, K. Amrutha, M. Chandanashree, K.R. Global Academy of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456357

Today, one of the top priorities of governments istrafficsafety. Given the importance of the subject, identifying the causes of road accidents has become the primary goal in reducing the damage caused by traffic accidents. Machine learning and data mining concepts are usedto recognize the various factors that influence road accidents andtheir severity. The application uses at odd Machine Learning algorithms which includes K-Nearest-Neighbor, Decision tree, Random forest, etc make predictions based on various parameters, within allthese models KNN gives the best accuracy. This informationcan be used to analyze future inputs and improve the system'soutput accuracy. This model can be improved further to send the accident report to the appropriate authorities, such as hospitals, ambulances, and insurance companies, and can thus be very useful in reducing accident fatality rates in the country. © 2022 IEEE.

关键词： Roads and streets

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Enhanced Heart Disease Classification: An Ensemble-Based Deep Learning Approach

Enhanced Heart Disease Classification: An Ensemble-Based Dee...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Nivaashini, M. Sathish Kumar, G. Uma Maheshwari, G. Soundariya, R.S. Kumar B, Arun Vijaya, G. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Karpagam Academy of Higher Education Faculty of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376135

The WHO's assessment indicates a rising prevalence of heart disease, exacerbated by challenges in detection and diagnosis in India due to population growth. Machine Learning (ML) is increasingly used for precise judgments in healthcare, aiming to accurately diagnose heart-related illnesses. This study proposes a Deep Learning (DL) ensemble method, combining ML classifiers to enhance accuracy. The ensemble model outperforms AdaBoost, XGBoost, Random Forest, Decision Tree, SVM, and Logistic Regression with 98.3% accuracy on a UCI dataset. Correlation-based Feature Selection (CFS) for relevant characteristic selection improves the model's accuracy, recall, precision, and f1-score. This research contributes to improved disease prediction and categorization in cardiovascular health, vital for effective healthcare interventions and outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Decrease the Prompt Uncertainty: Adversarial Prompt Learning for Few-shot Text Classification

Decrease the Prompt Uncertainty: Adversarial Prompt Learning...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Weng, Jinta Zhang, Zhaoguang Yaqi, Jing Niu, Chenxu Huang, Heyan Hu, Yue School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Cyber Engineering Guangzhou University China National Computer Network Emergency Response Technical Team Coordination Center of China China Beijing Institute of Technology School of Computer Science and Technology Beijing China Southeast Academy of Information Technology Putian China

ISBN: (纸本)9781665410205

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs. © 2024 IEEE.

关键词： Adversarial machine learning

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Single-shot single-beam coherent Raman scattering thermometry based on optically induced air lasing

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Light(science & Applications) 2024年第12期13卷 3253-3260页

作者： Xu Lu Yewei Chen Francesco Mazza Siyi He Zihan Li Shunlin Huang Quanjun Wang Ning Zhang Bo Shen Yuzhu Wu Jinping Yao Ya Cheng State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 12629 HS DelftThe Netherlands Institute of Modern Optics Nankai UniversityTianjin 300350China School of Microelectronics Shanghai UniversityShanghai 200444China

Thermometric techniques with high accuracy,fast response and ease of implementation are desirable for the study of dynamic combustion environments,transient reacting flows,and non-equilibrium ***,single-shot singlebeam coherent Raman scattering(SS-CRS)thermometry is developed,for the first time to our knowledge,by using air lasing as a *** show that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition *** SS-CRS thermometry consistently exhibits precision of<2.3%at different temperatures,but the inaccuracy grows with the increase in *** high measurement repeatability,1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal,spectral and spatial characteristics of air *** work opens a novel avenue for high-speed CRS thermometry,holding tremendous potential for fast diagnostics of transient reacting flows and plasmas.

关键词： scattering lasing beam

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Online Signature Recognition using Transformer Networks: An Overview 6

Online Signature Recognition using Transformer Networks: An ...

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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Bharadi, Vinayak Hamdule, Zohaib Kambli, Sujan Salvi, Raj Chavan, Raj Nimbalkar, Vrishali Finolex Academy of Management and Technology Department of Information Technology Ratnagiri India Finolex Academy of Management and Technology Dept. of Computer Science and Engineering Ratnagiri India

ISBN: (纸本)9798350359817

Online signature verification remains a pivotal component in digital security, ensuring the authenticity of digital transactions and documents. This paper offers a comprehensive overview of prevailing online signature verification techniques, with a particular emphasis on methods employing Deep Learning. RNNs, with their inherent capability to process sequential data, have been instrumental in improving the accuracy and efficiency of signature verification systems. However, as the digital forgery landscape becomes increasingly sophisticated, there is a pressing need to explore advanced neural architectures. In this context, the paper introduces a transformer-based approach for signature verification. Unlike traditional models, transformers, with their self-attention mechanisms, have the potential to capture intricate signature patterns and nuances, offering a promising avenue for future research and application. © 2023 IEEE.

关键词： Recurrent neural networks

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Optimizing VNF Service Provisioning in Mobile Edge Computing Networks Through PSO-RL 3

Optimizing VNF Service Provisioning in Mobile Edge Computing...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Abinaya, K. Dhanasekaran, S. Vasudevan, V. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputtur India Kalasalingam Academy of Research and Education Department of Information Technology Tamilnadu Srivilliputtur India

ISBN: (纸本)9798350361186

This study deals with setting up Virtual Network Function (VNF) services in Mobile Edge Computing (MEC) networks. It aims to balance different user needs in the everchanging MEC environment. Leveraging Particle Swarm Optimization-Reinforcement Learning algorithms (PSO-RL), our proposed system offers a dynamic provisioning approach adept at accommodating user mobility patterns and service delay prerequisites. The system's sequential flow comprises distinct stages designed for optimal functionality. The Firefly Algorithm efficiently maximizes utility by simplifying intricate issues and approximating optimal solutions. Additionally, our system implements Stochastic Gradient Descent and Simulated Annealing techniques for real-time throughput optimization, ensuring adaptability to ever-changing network conditions. Experimental simulations form a critical component of our study, providing a robust assessment of our proposed methodologies. Results obtained demonstrate substantial enhancements across critical metrics such as Throughput, Resource Utilization Efficiency, and End-to-End Delay. Varied scenarios encompassing different quantities of IoT users and mobile edge servers were simulated, reaffirming the efficacy of the PSO-RL method. These simulations underscore the potential of our proposed approach in significantly bolstering the performance and responsiveness of MEC networks. Ultimately, this study showcases promising prospects in addressing the intricate challenges associated with provisioning VNF services to mobile users in dynamic MEC environments. © 2024 IEEE.

关键词： Simulated annealing

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Ia-Mstm: Improved Adaptive Moment Estimation Enabled Mixed Attention-Based Long Short-Term Memory For Crop Yield Prediction 3

Ia-Mstm: Improved Adaptive Moment Estimation Enabled Mixed A...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vani, B.V. Guruprakash, C.D. Sri Siddhartha Academy of Higher Education Sri Siddhartha Institute of Technology Saraswathipuram Information Science and Engineering Karnataka Tumakuru572105 India Sri Siddhartha Institute of Technology Department Computer Science and Engineering Karnataka Tumakuru572105 India

ISBN: (纸本)9798350364569

Crop yield prediction has sought tremendous attention in recent years several approaches were developed for crop yield prediction. However, these techniques created certain limitations in real-time applications including a reduction in relative errors, and low prediction accuracy. Therefore, this research proposed a deep learning technique known as improved Adaptive Moment estimation enabled mixed attention-based long short-term memory (IA-MSTM) to predict crop yields. The mixed attention techniques combine the advantages of channel attention with the hard attention mechanism (HAM), which can capture long-term dependencies that are crucial for understanding crop growth dynamics over time. Additionally, the improved repeated Loss-based Adam optimizer adapts the learning rate during training, leading to quicker convergence compared to traditional methods. The performance of the IA-MSTM achieves a minimal error value in terms of Mean Absolute Error (MAE) of 2.00 for TP 90. © 2024 IEEE.

关键词： Prediction models

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