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A lightweight and efficient raw data collection scheme for IoT systems

Journal of Information and Intelligence 2024年第3期2卷 209-223页

作者： Yixuan Huang Yining Liu Jingcheng Song Weizhi Meng School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou 325027China School of Computer Science&Engineering LinYi UniversityLinyi 276000China Department of Aplied Mathematics and Computer Science Technical University of DenmarkKongens Lyngby 2800Denmark

With the prevalence of Internet of Things(loT)devices,data collection has the potential to improve people's lives and create a significant ***,it also exposes sensitive information,which leads to privacy *** approach called N-source anonymity has been used for privacy preservation in raw data collection,but most of the existing schemes do not have a balanced efficiency and *** this work,a lightweight and efficient raw data collection scheme is *** proposed scheme can not only collect data from the original users but also protect their ***,the proposed scheme can resist user poisoning attacks,and the use of the reward method can motivate users to actively provide *** and simulation indicate that the proposed scheme is safe against poison ***,the proposed scheme has better performance in terms of computation and communication overhead compared to existing *** efficiency and appropriate incentive mechanisms indicate that the scheme is practical for IoT systems.

关键词： data privacy Raw data collection loT systems Unlinkability

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Urdu Fake News Detection using TF-IDF Features and TextCNN 13

Urdu Fake News Detection using TF-IDF Features and TextCNN

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Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021

作者： Ilyas, Muhammad Abdullah Shahzad, Khurram Department of Computer Science University of the Punjab Lahore Pakistan Department of Data Science University of the Punjab New Campus Lahore Pakistan

Social media platforms are widely to exchange ideas and sharing news with each other. Consequently, any idea, post, or new posted on social media is likely to become viral in a short span of time. However, if a fake news becomes viral it can have consequences for the society. Therefore, the detection and eradication of fake news is desired. Recognizing the need for fake news detection, several studies have been conducted for this task in English and Western languages. However, the research in Urdu fake news detection has merely commenced. To promote research and development in this area, the second time in two years, Forum for Information Retrieval Evaluation 2021 has dedicated a track for fake news detection task for Urdu, called UrduFake’21. In this study, we have used three deep learning techniques for Urdu fake news detection, including the state-of-the-art Text CNN with three types of word embeddings, as well as TF-IDF features. The results of the experiments has shown that TextCNN with TF-IDF is the most effective technique that achieved an accuracy of 0.716 and macro average F1 score of 0.663. Furthermore, according to the results released by the organizers of UrduFake’21 our approach is ranked 2nd among the 19 submissions. © 2021 Copyright for this paper by its authors.

关键词： Convolutional neural networks

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Diagnostic Framework for Mumps Based on Deep Learning with Enhanced Mayfly Optimization Algorithm 1

Diagnostic Framework for Mumps Based on Deep Learning with E...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mercy, S. Sudha Madhavi, S. Keerthika, A. Prathima, Ch Prabhu, R.T. R.M.K Engineering College Department of Computer Science & Design Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Department of It Chennai India Aarupadai Veedu Institute of Technology Department of Ece Chennai India Department of Data Science Tirupati India Saveetha School of Engineering Simats Department of Ece Chennai India

ISBN: (纸本)9798350316469

The mumps virus causes a very infectious sickness. The enlargement of the parotid salivary glands can be quite uncomfortable. Mumps treatment centers on symptom management. The illness must take its natural course. While most people experience very moderate symptoms, some develop severe consequences. The mumps virus can be avoided with the use of the MMR vaccination. In this research, we employ a convolutional neural network (CNN) technique to better automate mumps detection. In order to automate mumps detection, the authors of this research suggest a convolutional neural network (CNN) trained using XGBoost Classifier data. Incorporating machine learning (ML) technique comparisons. A mumps dataset consisting of 50×50 RGB picture patches served as the basis for all the models. Quantitative findings from each approach were put through validation testing based on their respective effectiveness metrics. To improve the efficiency of the model, the Mayfly Optimization Algorithm is implemented. Results show that the suggested method is effective, with 97% accuracy, suggesting that it might help decrease diagnostic errors caused by human error. And the suggested system we've developed is so precise that it outperforms the CNN model, which only gets around 92% of the time. Consequently, the suggested method is 5% more accurate than the CNN Model. © 2023 IEEE.

关键词： Convolutional Neural Network Mayfly Optimization Algorithm Mumps Classification XGBoost Classifier

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Robust WCET Estimation via Ensemble Learning with Fractional-Order Legendre Function Kernels

Robust WCET Estimation via Ensemble Learning with Fractional...

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Applied Research in Electrical Engineering (AREE), National and International Conference on

作者： Amir Hussein Ataei Monfared Mahdi Movahedian Moghaddam Sheedeh Sharif-Bakhtiar Kourosh Parand Keivan Borna Department of Computer Science Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Cognitive Modeling Institute for Cognitive and Brain Sciences Shahid Beheshti University Tehran Iran

ISBN: (数字)9798350377170

ISBN: (纸本)9798350377187

Estimating Worst-Case Execution Time (WCET) as a regression problem has become increasingly challenging due to the complexity of modern hardware and software systems. Traditional statistical methods often fall short of providing accurate and reliable estimates. To address these limitations, ensemble learning methods have emerged as a promising approach for capturing edge cases in execution scenarios. This paper proposes a novel ensemble model that integrates fractional-order Legendre functions (FLF) to enrich feature representation. This integration reduces correlations between individual models within the ensemble, enhancing generalization capabilities. Our method achieves significant reductions in Mean Squared Error (MSE) for WCET estimates while maintaining high levels of safeness. Specifically, the proposed model maintained an error below one across most benchmarks, with safeness levels of $\% 100$ in 13 out of 16 benchmarks. These findings underscore the efficacy of our approach and its potential to provide robust and accurate WCET estimates for complex systems.

关键词： Support vector machines Accuracy Upper bound Estimation Benchmark testing Real-time systems Complexity theory Ensemble learning Time factors Kernel

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SPARQL Generation with Entity Pre-trained GPT for KG Question Answering

arXiv

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arXiv 2024年

作者： Bustamante, Diego Takeda, Hideaki Department of Computer Science PUC Santiago Chile Millenium Institute for Foundational Research on Data Santiago Chile Tokyo Japan

Knowledge Graphs popularity has been rapidly growing in last years. All that knowledge is available for people to query it through the many online databases on the internet. Though, it would be a great achievement if non-programmer users could access whatever information they want to know. There has been a lot of effort oriented to solve this task using natural language processing tools and creativity encouragement by way of many challenges. Our approach focuses on assuming a correct entity linking on the natural language questions and training a GPT model to create SPARQL queries from them. We managed to isolate which property of the task can be the most difficult to solve at few or zero-shot and we proposed pre-training on all entities (under CWA) to improve the performance. We obtained a 62.703% accuracy of exact SPARQL matches on testing at 3-shots, a F1 of 0.809 on the entity linking challenge and a F1 of 0.009 on the question answering *** Codes 68P20, 68T50 © 2024, CC0.

关键词： Knowledge graph

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Geo-parsing and analysis of road traffic crash incidents for data-driven emergency response planning

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Heliyon 2025年第4期11卷 e41067页

作者： Idakwo, Patricia Ojonoka Adekanmbi, Olubayo Soronnadi, Anthony David, Amos Department of Computer Science African University of Science and Technology Abuja Nigeria Data Science Nigeria AI Hub 33 Queens Street Alagomeji Yaba Lagos Nigeria

Road traffic crashes (RTCs) are a major public health concern worldwide, particularly in Nigeria, where road transport is the most common mode of transportation. This study presents the geo-parsing approach for geographic information extraction (IE) of RTC incidents from news articles. We developed two custom, spaCy-based, RTC domain-specific named entity recognition (NER) models: RTC NER Baseline and RTC NER. These models were trained on a dataset of Nigerian RTC news articles. Evaluation of the models’ performances shows that the RTC NER model outperforms the RTC NER Baseline model on both Nigerian and international test data across all three standard metrics of precision, recall and F1-score. The RTC NER model exhibits precision, recall and F1-score values of 93.63, 93.61 and 93.62, respectively, on the Nigerian test data, and 91.9, 87.88 and 89.84, respectively, on the international test data, thus showing its versatility in IE from RTC reports irrespective of country. We further applied the RTC NER model for feature extraction using geo-parsing techniques to extract RTC location details and retrieve corresponding geographical coordinates, creating a structured Nigeria RTC dataset for exploratory data analysis. Our study showcases the use of the RTC NER model in IE from RTC-related reports for analysis aimed at identifying RTC risk areas for data-driven emergency response planning. © 2024 The Authors

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CMRI Segmentation Domain Generalization by Random Style Transfer 12

CMRI Segmentation Domain Generalization by Random Style Tran...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Song, Haixin Li, Lei Luo, Zhiming Lian, Sheng Li, Shaozi Xiamen University Department of Artificial Intelligence Fujian Xiamen361005 China Fuzhou University College of Computer and Data Science Fujian Fuzhou350000 China

ISBN: (纸本)9798350310153

When training data and test data have different distribution, performance of neural networks may decrease significantly, and model is difficult to generalize knowledge. Domain generalization (DG) attempts to learn general and transferable knowledge from a variety of source domain data to enable the model to generalize to unseen domains. In this paper, considering both DG and few-shot scenario, we propose a random stylization block to augment existing styles through nonlinear combination of data, called style escape, so as to obtain more unseen styles for the model to learn consistency. We propose a heuristic gradient weighting strategy to optimize the training process of meta-learning model. The experimental results on M&Ms dataset show that segmentation performance of our model is significantly improved compared with baselines, and the model training cost is noticeably reduced, which fully verifies the effectiveness of our method. © 2022 IEEE.

关键词： Image segmentation

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An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques

An Evaluation Of Hospital Accreditation From The Survey With...

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International Joint Conference on computer science and Software Engineering (JCSSE)

作者： Dittaphong Prapunwattana Aurawan Imsombut Picha Suwannahitatorn Thammakorn Saethang Biomedical Data Science Master's Program Faculty of Science Kasetsart University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Thai Board of Prevention Medicine (Epidemiology) Phramongkutklao College of Medicine Bangkok Thailand

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring.

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Optimization techniques for preserving privacy in data mining 5

Optimization techniques for preserving privacy in data minin...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Devi, K.Renuka Balasamy, K. Prathyusha, M. Jeevitha, R. Balasubramanie, P. Eswaran, Malathi Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India MVSR Engineering College Department of IT Hyderabad India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode Perundurai India

ISBN: (纸本)9781665493604

data mining is one of the significant area where it plays a predominant role in extracting important factors and trends from large volume of data. This covers various areas such as healthcare, education, entertainment, finance, e-commerce applications etc., The data mining domain has used a variety of algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning techniques. Under healthcare arena, it deals with huge amount of sensitive data such as patients' data such as their name, age, health records. Those sensitive data have been utilized by the intruders for extracting the original data and also became a prey for the authorized access. Hence, the privacy is one of the serious concern that should be addressed. Various privacy preserving in data mining (PPDM) techniques such as anonymization, perturbation, condensation and cryptographic methods are available to protect those data. In this paper, the optimization techniques such as Genetic algorithm(GA) under evolutionary method and Particle swarm optimization(PSO) under meta heuristic method have been discussed and how it plays an important part in providing more optimal results by securing those sensitive and important information from the unauthorized access. © 2023 IEEE.

关键词： Genetic algorithms

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yujiao Chen, Mingzhe Wang, Sihua Liu, Yuchen Li, Gaolei Yin, Changchuan Quek, Tony Q.S. Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Network Beijing100876 China Singapore University of Technology and Design 487372 Singapore University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

ISBN: (纸本)9798350351255

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method. © 2024 IEEE.

关键词： Gaussian distribution

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