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2024 5th International Conference on Big Data Economy and information Management, BDEIM 2024

作者： Gao, Juan Guo, Xiangyun Xiang, Tianqi School of Computing Beijing Information Science & Technology University Beijing China School of Management Science and Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9798400711862

Construct a rational map of online public opinion on emergencies, reveals the evolutionary paths and developmental contexts of public opinion incidents, providing decision support for government departments in managing and guiding public sentiments. Select Sina Weibo as the data source, use octopus collector to collect Gansu earthquake Weibo data, and then perform data preprocessing. The causal relationships of the events were determined based on a rule-based template matching method, and a multi-round dialogue mechanism based on large language models was used to automatically extract causal event pairs. The K-means++ clustering algorithm was employed to generalize the events and construct a network public opinion event logic graph, analyzing the evolutionary paths of online public sentiments. The research findings indicate that public sentiment regarding the Gansu earthquake is characterized by multiple levels and complex causal relationships. The involvement of multiple causal chains contributes to a high degree of complexity in the evolution of public sentiment. By constructing an event logic graph, the development process of the Gansu earthquake public opinion event has been demonstrated, which can provide technical support for real-time monitoring, in-depth analysis, and precise response of public opinion. © 2024 Copyright held by the owner/author(s).

关键词： emergencies evolutionary pathways large model Multiple rounds of dialogue online public opinion

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Digital Twin-Driven Intrusion Detection for IoT and Cyber-Physical System

Digital Twin-Driven Intrusion Detection for IoT and Cyber-Ph...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Ramamoorthy, Saravanan Kandaneri Sindhu, L. Valarmathi, K. Gobinath, C. Umaeswari, P. College of Non-Medicine University: Texila American University Guyana Sona College of Technology Department of Information Technology Salem India S. A. Engineering College Department of Information Technology Chennai India Reva University School of Computing and Information Technology Bengaluru India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India

ISBN: (纸本)9798350360660

From smart homes to industrial automation, the efficiency and connectivity of many applications have been greatly improved by the fast expansion of the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Sophisticated cyberattacks are becoming more common in this networked ecosystem, thus strong security measures are needed. In order to protect CPS and IoT settings, this study presents a new intrusion detection method that makes use of Digital Twin technology. To build an adaptable and dynamic Intrusion Detection System (IDS), our suggested architecture combines Digital Twins with state-of-the-art machine learning methods. In order to accurately detect and categorize any threats, the IDS constantly learns from data in both the past and the present. When tested on benchmark datasets and in real-world Internet of Things (IoT) settings, the framework outperforms conventional intrusion detection system (IDS) solutions in terms of detection accuracy and false alarm rate reduction. To protect IoT and CPS infrastructures from ever-changing cyber threats, this scalable and resilient security solution is offered by this IDS that is powered by digital twins. We present ICNN-FCID, an Integrated Convolutional Neural Network for Fog computing Environment, a hybrid deep learning intrusion detection model for fog computing environments. © 2024 IEEE.

关键词： Network intrusion

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Creative Research on Automatic Detection of Flying Objects 9

Creative Research on Automatic Detection of Flying Objects

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9th International Conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Liu, Lu Lu, Yiyang Xu, Binrui Peng, Heyang Ren, Yuhan College of International Education SiChuan Normal University Chengdu China Faculty of Computing Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China Law School SiChuan Normal University Chengdu China

ISBN: (纸本)9798350376548

With the increasing development of China's modernisation and the growing demand for national defence security, it is very important to detect unidentified flying objects in the air and eliminate potential threats to ensure national security. In order to solve the dual problems of high cost of manual inspection and poor performance of the detection model with low accuracy, this paper proposes a way to automate the detection of flying objects by using the YOLOv5 target detection model, improving the loss function CIOU-Loss to EIOU-Loss by default to speed up the convergence speed of the model, and at the same time adopting a variety of novel data enhancement methods to improve the accuracy. This paper also compares the performance of the YOLOv5 model before and after the improvement in the training and flying objects detection process to demonstrate the effectiveness of the improvement. © 2024 IEEE.

关键词： Object tracking

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Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangzhou China Faculty of Chinese Language and Culture Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9780000000002

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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A Scalable and Lightweight Authentication Architecture for the Internet of Things (IoT) in Smart Home Applications 2

A Scalable and Lightweight Authentication Architecture for t...

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2nd Zimbabwe Conference on information Communication and technology, ZCICT 2023

作者： Gamundani, Attlee M. Phillips, Amelia Muyingi, Hippolyte N. Namibia University of Science and Technology Faculty of Computing & Informatics Cyber Security Department Windhoek Namibia University of the Cumberlands School of Computer and Information Sciences WilliamsburgKY United States Namibia University of Science and Technology Faculty of Computing & Informatics Windhoek Namibia

ISBN: (纸本)9798350318128

In the evolving landscape of smart home applications, ensuring robust and efficient authentication within the Internet of Things (IoT) environments is paramount. This paper presents a scalable, lightweight authentication architecture for IoT-driven smart homes. The proposed architecture, drawing inspiration from cloud computing mechanisms, introduces a trusted, smart home agent, integrating the Data-information-Knowledge-Wisdom (DIKW) model to facilitate efficient and secure data exchange within smart homes. Our solution addresses the challenges of minimal computation and processing overheads, essential in resource-constrained IoT devices, and focuses on device-level authentication to reduce computational costs. A comparative analysis with existing models demonstrates the superiority of our architecture in terms of computational efficiency, making it an ideal candidate for scalable deployments. The architecture successfully navigates the complexities of ensuring secure communication among autonomous IoT devices, balancing the dual needs of computational efficiency and robust security, thereby promising resilience against common security threats in the smart home ecosystem. © 2023 IEEE.

关键词： Authentication

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LDREG: LOCAL DIMENSIONALITY REGULARIZED SELF-SUPERVISED LEARNING 12

LDREG: LOCAL DIMENSIONALITY REGULARIZED SELF-SUPERVISED LEAR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Huang, Hanxun Campello, Ricardo J.G.B. Erfani, Sarah Ma, Xingjun Houle, Michael E. Bailey, James School of Computing and Information Systems The University of Melbourne Australia Department of Mathematics and Computer Science University of Southern Denmark Denmark School of Computer Science Fudan University China Department of Computer Science New Jersey Institute of Technology United States

Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities. Dimensional collapse - also known as the "underfilling" phenomenon -is one of the major causes of degraded performance on downstream tasks. Previous work has investigated the dimensional collapse problem of SSL at a global level. In this paper, we demonstrate that representations can span over high dimensional space globally, but collapse locally. To address this, we propose a method called local dimensionality regularization (LDReg). Our formulation is based on the derivation of the Fisher-Rao metric to compare and optimize local distance distributions at an asymptotically small radius for each data point. By increasing the local intrinsic dimensionality, we demonstrate through a range of experiments that LDReg improves the representation quality of SSL. The results also show that LDReg can regularize dimensionality at both local and global levels. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems

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Sustainable Operations and Computers 2024年第1期5卷 88-101页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Al-Obeidat, Feras Alomari, Osama Ahmad Abasi, Ammar Kamal Tubishat, Mohammad Elgamal, Zenab Alomoush, Waleed Department of Data Science and Artificial Intelligence University of Petra Amman11196 Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University P.O. Box 50 Al-Huson Irbid Jordan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates School of Computing Skyline University College Sharjah United Arab Emirates School of Information Technology Skyline University College Sharjah P.O. Box 1797 United Arab Emirates Abu Dhabi United Arab Emirates

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives. © 2024 The Author(s)

关键词： Multiobjective optimization

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Identification of Cyberbullying and Finding Target User's Intention on Public Forums 6

Identification of Cyberbullying and Finding Target User's In...

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Krishnasamy, Lalitha Kiruthika, M. Manjunatha Swamy, C. Vigneshwaran, T. Sharon Roji Priya, C. Vijay Anand, D. School of Engineering and Technology Department of CSE Christ University Bengaluru India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Tamilnadu Kattankulathur India Kongu Engineering College Department of Information Technology Erode India

ISBN: (纸本)9798350304480

Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. © 2023 IEEE.

关键词： bag of words cyberbullying feature extraction sentiment analysis SVM

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Performance evaluation of different deep learning models used for the purpose of healthy and diseased leaves classification of Cherimoya (Annona Cherimola) plant

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Neural computing and Applications 2025年第6期37卷 4531-4544页

作者： Chouhan, Siddharth Singh Singh, Uday Pratap Jain, Sanjeev School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Department of Mathematics Central University of Jammu Jammu and Kashmir Samba181143 India Department of Computer Science and Information Technology Central University of Jammu Jammu and Kashmir Samba181143 India

Controlling plant leaves disease helps in upholding their health. This augments the overall strength of the plant productivity both in terms of higher quality and quantity. Recently, with the expansion of high-end computing devices, Artificial Intelligence (AI) techniques have allied in almost all applications. In agriculture, AI has been used in crop surveillance, soil health monitoring, nutrient deficiency estimation, flower and fruits quality assessment, weed estimation, and crop health diagnosis among all. Therefore, in this work, five deep learning approaches, namely EfficientNet, MobileNetV2, BiT, EANet, and Swin Transformers, have been used for the classification of healthy and diseased leaves of Cherimoya plant. EANet model with precision = 0.9929%, recall = 0.9536%, F1-score = 0.9846%, training accuracy = 0.9775%, and testing accuracy = 0.9689% achieved superior performance among all. We believe that the proposed work can be very useful in providing an accurate and timely plant disease diagnosing system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fruits

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Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier

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Intelligent Automation & Soft computing 2023年第5期36卷 1931-1949页

作者： Vaibhav Rupapara Furqan Rustam Abid Ishaq Ernesto Lee Imran Ashraf School of Computing and Information Sciences Florida International UniversityUSA Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Computer Science Broward CollegeBroward CountyFloridaUSA Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Korea

Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health *** the last few years,an alarming increase is observed worldwide with a 70%rise in the disease since 2000 and an 80%rise in male *** untreated,it results in complications of many vital organs of the human body which may lead to *** detection of diabetes is a task of signiﬁcant importance to start timely *** study introduces a methodology for the classiﬁcation of diabetic and normal people using an ensemble machine learning model and feature fusion of Chi-square and principal component *** ensemble model,logistic tree classiﬁer(LTC),is proposed which incorporates logistic regression and extra tree classiﬁer through a soft voting *** are also performed using several well-known machine learning algorithms to analyze their performance including logistic regression,extra tree classiﬁer,AdaBoost,Gaussian naive Bayes,decision tree,random forest,and k nearest *** addition,several experiments are carried out using principal component analysis(PCA)and Chi-square(Chi-2)fea-tures to analyze the inﬂuence of feature selection on the performance of machine learning classiﬁ*** indicate that Chi-2 features show high performance than both PCA features and original ***,the highest accuracy is obtained when the proposed ensemble model LTC is used with the proposed fea-ture fusion framework-work which achieves a 0.85 accuracy score which is the highest of the available approaches for diabetes *** addition,the statis-tical T-test proves the statistical signiﬁcance of the proposed approach over other approaches.

关键词： Diabetes mellitus prediction feature fusion ensemble classiﬁer principal component analysis chi-square

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