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arXiv 2025年

作者： He, Qiyuan Yu, Jianfei Wang, Wenya College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Nanjing University of Science and Technology China

Integrating large language models (LLMs) with rule-based reasoning offers a powerful solution for improving the flexibility and reliability of Knowledge Base Completion (KBC). Traditional rule-based KBC methods offer verifiable reasoning yet lack flexibility, while LLMs provide strong semantic understanding yet suffer from hallucinations. With the aim of combining LLMs’ understanding capability with the logical and rigor of rule-based approaches, we propose a novel framework consisting of a Subgraph Extractor, an LLM Proposer, and a Rule Reasoner. The Subgraph Extractor first samples subgraphs from the KB. Then, the LLM uses these subgraphs to propose diverse and meaningful rules that are helpful for inferring missing facts. To effectively avoid hallucination in LLMs’ generations, these proposed rules are further refined by a Rule Reasoner to pinpoint the most significant rules in the KB for Knowledge Base Completion. Our approach offers several key benefits: the utilization of LLMs to enhance the richness and diversity of the proposed rules and the integration with rule-based reasoning to improve reliability. Our method also demonstrates strong performance across diverse KB datasets, highlighting the robustness and generalizability of the proposed framework. © 2025, CC BY.

关键词： Semantics

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Edge Learning-Based Efficient data Imputation of Water Quality 7th

Edge Learning-Based Efficient Data Imputation of Water Quali...

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7th International Conference on computing, Control and Industrial engineering, CCIE 2023

作者： Wang, Yongsheng Chen, Zhen Liu, Limin Gao, Jing Liu, Guangwen Xu, Zhiwei School of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100000 China

ISBN: (纸本)9789819927296

Water quality monitoring is critical for developing effective surface water management strategies, whereas data preprocessing is the foundation for monitoring water quality. Water quality monitoring usually suffers from abnormal and missing values in the collected data of surface water quality, and thus an efficient data imputation method for water quality data is highly desired. To handle these challenges, we propose an edge learning-based data imputation method. By using VAE as the generator of WGAN-LP, the performance of value imputation of water quality is generalized and improved. In addition, we demonstrate the performance of the proposed method on two real-world datasets, collected from Wuliangsu Lake and Taihu Lake in Inner Mongolia. Compared with state of the art, the enhanced imputation accuarcy of water quality is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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A Study on a Reduced Indicator System for the Network Security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Hongbin Bai, Yikang Zhao, Dongmei Liu, Bin Xu, Ying Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (纸本)9798350327380

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time. © 2023 IEEE.

关键词： Network security

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Multilingual Sentiment Analysis using Deep-Learning Architectures 5

Multilingual Sentiment Analysis using Deep-Learning Architec...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Dominic, Praveen Purushothaman, Niranjan Kumar, Anish Skanda Anil Prabagaran, A. Blessy, J. Angelin John, A. School of Graduate Studies MSc in Big Data Analytics Trent University Ontario Canada Visa Consolidated Support Services India Pvt. Ltd Bengaluru India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665474672

Machine learning techniques such as NLP (Natural language processing) play a key role in a context where mining social media data could add great value to governments of the world countries. The posts and tweets shared by the people on social media can be mined to infer the valuable 'mindset' of the people which is much required for any ruling government in the world. The objective of this study is to conduct sentiment analysis to mine the sentiment of the people regarding the ongoing war between Russia and Ukraine, using machine learning techniques. The idea is to analyze and infer if the countries have reacted in some way, considering the sentiment of their citizens, in the context of economic effects. The pipeline of the implementation associated starts with the data collection from social media such as Twitter and Reddit using Snscraper and the PRAW (Python Reddit API Wrapper). The larger posts from Reddit are handled by implementing suitable text summarization techniques. Sentiment analysis is performed for the social media data using the BERT transformer model. The non-English posts are translated to English using neural machine translation. Also, sentiment analysis is performed at various granularities such as the location and the people that are tagged using Named Entity Recognition techniques. Finally, a comparative analysis of the world countries' sentiment and their corresponding reliance on Russian oil is performed. © 2023 IEEE.

关键词： Sentiment analysis

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Drone Gesture Control using OpenCV and Tello 8

Drone Gesture Control using OpenCV and Tello

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8th Conference on Information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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data on the Move: Traffic-Oriented data Trading Platform Powered by AI Agent with Common Sense

arXiv

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arXiv 2024年

作者： Yu, Yi Yao, Shengyue Zhou, Tianchen Fu, Yexuan Yu, Jingru Wang, Ding Wang, Xuhong Chen, Cen Lin, Yilun Urban Computing Lab Shanghai Artificial Intelligence Laboratory Shanghai China The School of Data Science and Engineering East China Normal University China The School of Data Science and Engineering East China Normal University Shanghai China

In the digital era, data has become a pivotal asset, advancing technologies such as autonomous driving. Despite this, data trading faces challenges like the absence of robust pricing methods and the lack of trustworthy trading mechanisms. To address these challenges, we introduce a traffic-oriented data trading platform named data on The Move (DTM), integrating traffic simulation, data trading, and Artificial Intelligent (AI) agents. The DTM platform supports evident-based data value evaluation and AI-based trading mechanisms. Leveraging the common sense capabilities of Large Language Models (LLMs) to assess traffic state and data value, DTM can determine reasonable traffic data pricing through multi-round interaction and simulations. Moreover, DTM provides a pricing method validation by simulating traffic systems, multi-agent interactions, and the heterogeneity and irrational behaviors of individuals in the trading market. Within the DTM platform, entities such as connected vehicles and traffic light controllers could engage in information collecting, data pricing, trading, and decision-making. Simulation results demonstrate that our proposed AI agent-based pricing approach enhances data trading by offering rational prices, as evidenced by the observed improvement in traffic efficiency. This underscores the effectiveness and practical value of DTM, offering new perspectives for the evolution of data markets and smart cities. To the best of our knowledge, this is the first study employing LLMs in data pricing and a pioneering data trading practice in the field of intelligent vehicles and smart cities. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart city

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Fabric4show: real-time vision system for fabric defect detection and post-processing

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Lin, Huaizhou Cai, Dan Xu, Zengmin Wu, Jinsong Sun, Lixian Jia, Haibin School of Materials Science and Engineering Guangxi Key Laboratory of Information Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Center for Applied Mathematics of Guangxi (GUET) Guilin China Anview.ai Guilin China School of Artificial Intelligence Guilin University of Electronic Technology Guilin China Department of Electrical Engineering University of Chile Santiago Chile

The exploration of computer vision applications for fabric defect detection has immense potential value. However, current relevant research in this area has primarily focused on detection models that aim for high detection accuracy and algorithmic efficiency, while neglecting the practical industrial production requirements. Therefore, we propose a fabric defect detection and post-processing system that integrates an optimized region with convolutional neural network (CNN) features (i.e., Faster R-CNN) for defect detection, defect localization and detection model evaluation. In addition, the proposed intelligent system incorporates novel approaches, such as a rearranged fabric dataset, anomaly detection, recommended clipping region division, and a replenishment device. This study illustrates an example of artificial intelligence (AI)-driven automated technology in fabric manufacturing. The accuracy and detection speed of different detection models under identical hardware conditions are evaluated and compared with related work. Experimental results demonstrate that the proposed approach achieves comparable performance to other models, while significantly reducing computational resource requirements. The potential efficiency of using two-stage networks on hardware systems for fabric defect detection tasks is highlighted, which is likely to have relevant implications for the textile industry. © The Author(s) 2024.

关键词： Automation Computer vision Defect detection Fabric manufacturing Faster R-CNN

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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Computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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