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intelligent Career Advisory System: Enhancing Career Matching with BERT and Graph Neural Networks

Intelligent Career Advisory System: Enhancing Career Matchin...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 ieee international conference on

作者： Min Hua Mideth Abisado College of Computing and Information Technologies National University Manila Philippine School of Information Engineering Xinyang Agriculture and Forestry University Xinyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Traditional career recommendation systems often fail to capture dynamic skill demands, subtle query semantics, and potential ethical and bias issues. In this paper, we propose a hybrid model that integrates BERT for semantic understanding and Graph Neural Networks (GNN) for relational reasoning to construct a dynamically updated career knowledge graph. The graph is built by fusing multi-source data from LinkedIn, O*NET, and synthetic data generated via GPT-4, and a cross-modal attention mechanism is used to fuse text and graph features. Experiments on the CareerQA dataset, consisting of 50,000 annotated queries, demonstrates that our model improves the Fl-score by 18.7% and achieves an NDCG@5 of 0.921. A user study involving 1,200 participants further validates the system's practical effectiveness with an 89.2 % positive feedback rate.

关键词： Ethics Engineering profession Social networking (online) Fuses Semantics Knowledge graphs Graph neural networks Real-time systems Recommender systems Synthetic data

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Multi-constraint Optimization for Energy Scheduling of Plug-In Electric Vehicles 8th

Multi-constraint Optimization for Energy Scheduling of Plu...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Linxin Yang, Zhile Guo, Yuanjun Xiao, Qinge Zhu, Juncheng Faculty of Data Science City University of Macau Taipa999078 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

ISBN: (纸本)9789819602315

The integration of a substantial number of plug-in electric vehicles (PEVs) into power grid scheduling introduces complexities due to stochastic charging and discharging behaviors, which pose significant challenges to the stability and reliability of the power system. The PEV scheduling optimization extends the traditional combinatorial optimization problem, adding constraints and variables, and becomes a multi-constraint unit commitment (MCUC)problem. In this paper, a novel PEV scheduling optimization framework is proposed to address MCUC problem, taking economic cost and carbon emissions as the objectives, a multi-constraint mathematical model is established. Meanwhile, a new binary level-based learning optimization algorithm is proposed to solve flexible scheduling of PEVs in power systems. Finally, considering different numbers of units, the superiority of the proposed optimization algorithm is verified by comparison with different algorithms. The feasibility and effectiveness of the proposed solution are also analyzed before and after scheduling to verify its potential in solving the MCUC problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Fault Localization Technique in Smart Active Distribution System with data Uncertainties

Fault Localization Technique in Smart Active Distribution Sy...

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Power, Control and computing Technologies (ICPC2T), conference on

作者： Arya Bharti Archana Tamrakar Saumendra Sarangi Electrical Engineering Department MNNIT Allahabad Prayagraj India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The necessity for a reliable power supply in active distribution systems requires information on precise fault location to restore the power following a fault. However, with inverter based DGs the modulated voltage and current limits the faulty section and fault location technique. Smart grid initiatives have added intelligent Electronic Devices (IEDs) and SCADA systems through communication networks which collects large sets of data which improves the monitoring of the whole system and this can also be applicable to obtain the fault location. However, the existence of errors in data presents a notable obstacle in locating the faults. Hence, to estimate the accurate data, weighted least squares and the chi-square probability density function-based approach are applied and with accurate estimated data, faulty line inductance is computed using the least squares (LSQ) technique. The proposed method is highly accurate for data uncertainties, high resistance fault, and bidirectional power flow. Further, the method is compared with a Current distribution technique (CDT) based impedance/ reactance fault location method, which is beneficial for close-infault. The efficacy of this approach is demonstrated through simulation of the ieee 33-bus Distribution System using MATLAB/SIMULINK R2023a.

关键词： Resistance Accuracy Uncertainty Power supplies Power control SCADA systems Fault location Phasor measurement units Smart grids Reliability

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Privacy and Security Challenges in Large Language Models

Privacy and Security Challenges in Large Language Models

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ieee Annual computing and Communication Workshop and conference (CCWC)

作者： Vishal Rathod Seyedsina Nabavirazavi Samira Zad Sundararaja Sitharama Iyengar Florida International University

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Large Language Models (LLMs) are at the forefront of artificial intelligence advancements, demonstrating exceptional capabilities in natural language understanding and generation across diverse domains such as healthcare, finance, and customer service. However, their deployment introduces substantial secu-rity and privacy risks, including prompt injection, data leakage, and unauthorized data disclosures. These vulnerabilities highlight the need for robust frameworks to safeguard sensitive data and prevent misuse. This paper provides a comprehensive analysis of the security and privacy challenges in LLMs, examines existing mitigation strategies such as intelligent LLM firewalls, differen-tial privacy, and OW ASP-based security principles, and discusses future directions for ethical and secure LLM deployment. By addressing these challenges in detail, we identify gaps in current practices and propose a roadmap for the secure and responsible deployment of LLMs in high-stakes applications. Our findings underscore the importance of tailored security frameworks and privacy-preserving techniques to ensure the ethical and reliable use of LLMs in sensitive environments. Additionally, this pa-per emphasizes the significance of a human-in-the-loop (HITL) approach to ensure accountability and accuracy, particularly in critical domains. The discussion extends to emerging technologies such as retrieval-augmented generation (RAG) and adaptive threat detection systems, which hold promise for enhancing the security and ethical deployment of LLMs.

关键词： Industries Ethics Privacy Technological innovation Firewalls (computing) Large language models Computational modeling Medical services Threat assessment Security

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data Security Protection System in Human-Machine-Object Fusion intelligent Demand Engineering System

Data Security Protection System in Human-Machine-Object Fusi...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Yongjun Qi Chenggao Li Hailin Tang Junxuan Shen Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In the engineering system of human-machine physical intelligence requirements, the multi-source and high complexity of data pose significant security risks. Therefore, this article designs a comprehensive data security protection system. Firstly, this article chooses a dynamic encryption strategy based on role-based access control. Then, this article combines differential privacy algorithm to construct a privacy protection model to ensure the privacy of data during sharing and processing. Subsequently, this article proposes a trusted data transmission protocol. Finally, this article introduces federated learning techniques to further improve the security and robustness of data in distributed scenarios. The experimental results show that the system performs well in terms of data security, privacy protection, and processing efficiency. Especially under high-intensity attacks, the system successfully detected and prevented approximately 98% of tampering. In the above data conclusions, the research method has significant advantages in encryption performance, achieving a better balance between privacy protection and data accuracy, demonstrating strong application prospects.

关键词： Differential privacy Protocols Federated learning data security Heuristic algorithms Robustness Encryption data communication Requirements engineering Protection

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Enhancing Personalized Agriculture With Cutting-Edge Hybrid Machine Learning Models And intelligent Edge computing For Sustainable Farming

Enhancing Personalized Agriculture With Cutting-Edge Hybrid ...

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data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： Vijayalakshmi K Maria Emilia Camargo Walter Priesnitz Filho Mithileysh Sathiyanarayanan Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Universidade Federal University of Santa Maria Brazil South America MIT Square London City University of London England United Kingdom

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The rapid advancement of artificial intelligence and edge computing has enabled personalized agriculture, optimizing farming practices by tailoring interventions to individual crop needs. Traditional methods often lead to inefficiencies in resource utilization, pest control, and irrigation management. This paper introduces a hybrid machine learning framework integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models with edge computing to enable real-time, site-specific decision-making. IoT sensors collect real-time data on soil moisture, temperature, and humidity, while aerial drones with multispectral imaging provide insights into crop health. XGBoost processes this data for soil health classification and pest risk prediction, achieving 92% accuracy. The hybrid CNN-LSTM model enhances irrigation and pest control predictions by 15% over standalone models, allowing farmers to make more precise, data-driven decisions. Field trials demonstrate a 25% increase in crop yield and a 30% reduction in water usage compared to conventional techniques. Edge computing ensures low-latency processing, enabling real-time implementation without cloud-based constraints. This research bridges the gap between real-time data acquisition, processing, and decision-making, offering a scalable, cost-effective solution for sustainable agriculture by reducing resource wastage, improving food security, and promoting environmentally responsible farming.

关键词： Temperature sensors Computational modeling Predictive models Pest control Real-time systems Convolutional neural networks Resource management Long short term memory Farming Edge computing

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

ISBN: (纸本)9789819660049

The proceedings contain 29 papers. The special focus in this conference is on intelligent Information and database systems. The topics include: GAMER-Pong: Game Adjustment by Monitoring Emotional Response;Machine Learning Algorithms Comparison for Hand sEMG-Recorded Movements Classification;boosting Face Super-Resolution and Identification with Knowledge Distillation and Frequency Domain Loss;improving Efficiency Image Captioning by Using Attention Mechanism Combined with Knowledge Graph;integrating Topological data Analysis and Deep Learning: A Case Study in Cardiovascular Disease Prediction at Thu Duc Hospital;boosting Lightweight Multi-branch Network by Integrating Segment, Skeleton, and Attribute Information;continuous Recognition of Mouth Patterns in Japanese Sign Language for Visual Communication;Towards an UAV Visual Navigation System Based on Map Processing and Spatial Matching Techniques: A Literature Review;image Enhancement with Boosted Schrödinger Bridge;kernel-Level Energy-Efficient Neural Architecture Search for Tabular dataset;estimation of Distribution Algorithms with Overlapped Subpopulations;energy-Efficient Edge Query Processing for Smart City Using Query Prediction;influence Maximization with Fairness Cost on Groups in Online Social Networks;TMMP: Efficient Continual Learning via Mixture of LoRA Experts and Top Maximum Magnitude Parameter Fusion;ADMEdge: Accelerating Edge computing Through Co-design for Enhanced data Mobility;the Recursive Scheme of Clustering;TOPSIS-Inspired Socio-cognitive Mutation Operator for Metaheuristics;feature-Based Drift Detection in Non-stationary data Streams Using Multiple Classifiers: A Comprehensive Analysis;a Feature Transformation Technique for Improving Ensemble Learning systems;explainable Artificial Intelligence to Improve Interpretability in Predictive Mutation Testing;an Analysis of Class Imbalance Challenges in Predictive Mutation Testing;efficient Vietnamese Name Detection Using Highly Discriminative N-Gra

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

17th Asian Conference on Intelligent Information and Databas...

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17th Asian conference on intelligent Information and database systems, ACIIDS 2025

ISBN: (纸本)9789819660070

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Audio-visual information integration for humanoid robot gaze shift

Audio-visual information integration for humanoid robot gaze...

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international conference on Energy, Power, Environment, Control and computing (ICEPECC 2025)

作者： Noman Amjad Syed Muhammad Wasif Zubair Mehmood Muhammad Jehanzeb Irshad Irfan Qaiser Ali Asghar Muhammad Waqas Jabbar Intelligent Systems Laboratory Department of Electrical Engineering University of Gujrat Gujrat Pakistan

ISBN: (数字)9781837243150

A humanoid robot is being designed capable of integrating audio-video signal. To develop a model for sound source localization in humanoid robot. Two USB Sound Card microphones are used for sound acquisition and two cameras are used for object detection. A Raspberry-pi is used as a central unit for image processing and sound source localization. When the video data comes from cameras, it is displayed on the screen in frames, and its image processing is being done in parallel. When the required objects image processing and detection is done, then it came to the tracking process. To track the detected object the centre position is defined and in code the error is defined that the quantity of error is encountered by adjusting the positions of servos. A PWM model had adopted for precise and accurate results. The results shows that when the red object came in front of camera a green rectangle is formed around it and a vertical line a centre, when the object moves the green line moves along the object, due to the movement of green line away from the centre position the servos position is changed to make the green vertical line came to its origin.

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Construction of an Innovation Information Mining Platform Integrating Knowledge Graph

Construction of an Innovation Information Mining Platform In...

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Electronics and Renewable systems (ICEARS), international conference on

作者： Dawei Wan Peisen Huang Sichuan University of Arts and Science Dazhou China

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the continuous expansion of data mining scenarios, an accurate data analysis algorithm framework is urgently needed. In this study, the construction of an innovation information mining platform integrating knowledge graph is discussed. The designed platform combines multiple layers of knowledge fusion, matching and problem solving. This architecture can greatly improve the intelligent extraction and mining of innovative information. The designed platform mainly consists of four key parts: 1. At the data acquisition level, network logs are analyzed through Web data mining. At the same time, GoAccess tool is used to extract access logs and conduct preliminary analysis. 2. At the data mining level, this study uses a probabilistic graph model based on a word pair model to perform cluster analysis on large-scale text data. At the same time, the innovative information points are classified through calorific value calculation and information entropy theory to obtain the mining effect. 3. At the system improvement level, we integrate knowledge graphs and data mining algorithms, and use the Jena inference engine to reason and complete data relationships. 4. At the platform optimization level, the cloud platform architecture of this study further optimizes the allocation of computing resources and application deployment, while using Kubernetes and Docker container technologies to ensure the high performance of the system. Through data mining accuracy test, K-mean and FCM algorithms were used to verify the algorithm effect of this study.

关键词： Technological innovation Analytical models Accuracy data analysis Knowledge graphs Computer architecture data models data mining Resource management Testing

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