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Library User Behavior and Service optimization Using Artificial Intelligence 4

Library User Behavior and Service Optimization Using Artific...

引用

4th IEEE International conference on Electronic Communications, Internet of things and Big Data, ICEIB 2024

作者： Wang, Ping Shandong Institute of Commerce and Technology Jinan China

ISBN: (纸本)9798350360721

Library user behavior was investigated using artificial intelligence (AI) technology to propose corresponding service optimization strategies. through data collection and analysis, the behavioral characteristics of users' borrowing, querying, and reading were determined. Machine learning and data mining were used to identify and predict the patterns of user behavior and provide personalized recommendations and services. the effectiveness and feasibility of the proposed strategies were verified in case studies and field research. © 2024 IEEE.

关键词： Behavioral research

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Detection of AI-Generated Text Using Large Language Model 4

Detection of AI-Generated Text Using Large Language Model

引用

4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Prajapati, Manish Baliarsingh, Santos Kumar Dora, Chinmayee Bhoi, Ashutosh Hota, Jhalak Mohanty, Jasaswi Prasad Kiit Deemed to Be University School of Computer Engineering Bhubaneswar751024 India Centurion University of Technology and Management Department of Electronics & Communication Engineering Bhubaneswar752050 India Gitam Deemed to Be University Department of Computer Science & Engineering Visakhapatnam752050 India

ISBN: (纸本)9798350349856

A large language model (LLM) is a trained deep-learning model that understands and generates text in a human-like fashion. Due to the significant advancements of LLM, it becomes a challenging task to distinguish human-written content from artificial intelligence (AI) generated content. In this work, we leverage the machine learning (ML) models to reliably identify whether an essay is authored by a human being or by an LLM. Concerns about LLMs replacing human tasks, especially in education persist. However, optimism remains for their potential as tools to enhance writing skills. An academic worry is LLMs facilitating plagiarism due to their extensive training in text and code datasets. Using diverse texts and unknown generative models, we replicate typical scenarios to encourage feature learning across models. In a study involving human subjects, we demonstrate that the annotation scheme offered by generative textual likelihood ratio (GLTR) enhances the human detection rate of fake text from 74% to 99% without requiring any previous training. GLTR is open source and publicly deployed, already finding widespread use in detecting generated outputs. © 2024 IEEE.

关键词： Deep learning

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Porting and optimization of Electric Power Consumers Anomaly Behaviour Monitoring on ARM Platform 4

Porting and Optimization of Electric Power Consumers Anomaly...

引用

4th International conference on intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Long, Yujiang Zhong, Ye Wei, Wei Zhan, Zhu Liu, Bin Information Center of Guizhou Power Grid Co Ltd. Guiyang China Fangxin Technology Co Ltd. Changsha China

ISBN: (纸本)9798350343113

With the development of smart grid, finer grid monitoring becomes possible. We propose a framework for abnormal behavior monitoring of power users, and carry out targeted deployment and optimization on the ARM platform. In order to align the anomaly detection performance of the model with that of humans, we designed a human-in-the-loop pipeline to achieve more accurate anomaly detection results with a small number of expert annotations. We validate the effectiveness of the proposed method on the electricity data of a region in China. © 2023 IEEE.

关键词： Anomaly detection

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Advanced Deep learning Techniques for Accurate Detection of Wheat Leaf Diseases 4th

Advanced Deep Learning Techniques for Accurate Detection of ...

引用

4th International conference on intelligent Systems and Pattern Recognition-ISPR

作者： Khaoula, Tabet Soltane, Merzoug Echahid Cheikh Larbi Tebessi Univ Lab Vis & Artificial Intelligence LAVIA Tebessa 12000 Algeria

ISBN: (纸本)9783031821523;9783031821530

Nowadays agriculture field is one of the important fields that plays a crucial role in the economics of countries. therefore the study of diseases related to plants, fruits and vegetables is of great importance in improving the quality of products. thus in this paper we focuses on the detection of leaf diseases using advanced deep learning techniques, specifically convolutional neural networks (CNN), Inception V3, and YOLOv8 architectures, this study focuses on the identification and classification of common wheat leaf diseases such as Septoria and Stripe rust, as well as distinguishing healthy leaves. the results demonstrate significant improvements in disease detection accuracy, offering a promising tool for farmers and agronomists.

关键词： Deep learning Convolutional neural networks (CNN) InceptionV3 YOLOv8 Wheat Leaf Diseases

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Research on Unmanned Positioning Technology Based on Factor Graph optimization 4

Research on Unmanned Positioning Technology Based on Factor ...

引用

4th International conference on intelligent Computing and Human-Computer Interaction, ICHCI 2023

作者： Sima, Haodong Lu, Jingwen Guo, Chence CSSC Haiwei Tech Co. LTD. Zhengzhou450000 China

ISBN: (纸本)9798350343113

A fusion positioning algorithm based on factor graph optimization is proposed to tackle the issues of low positioning accuracy and poor environmental adaptability associated with a single sensor in the unmanned positioning system. this algorithm integrates data from multiple sources, including the Global Positioning System (GPS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), and wheel tachometer. through front-end processing and back-end optimization, the algorithm significantly enhances the accuracy and adaptability of the unmanned positioning system. Validated under a scenario of the missing differential signal and laser point cloud features, the fusion positioning algorithm, harnessing factor graph optimization, exhibits substantial accuracy improvements of 84% and 63% over laser-based Simultaneous Localization and Mapping (SLAM) and GPS, respectively. © 2023 IEEE.

关键词： Global positioning system

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Citation Based Scientific Document Summarization Using Deep learning 4th

Citation Based Scientific Document Summarization Using Deep ...

引用

4th International conference on Advanced Network Technologies and intelligent Computing-ANTIC-Annual

作者： Jyoti, Divya Srivastava, Jyoti Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Dept Comp Sci & Engn Hamirpur 177005 Himachal Prades India

ISBN: (纸本)9783031837920;9783031837937

With the rapid increase in the volume of scientific literature, researchers face challenges in keeping up with the latest advancements while summarizing the documents. Scientific document text summarization offers a solution by providing concise and informative summaries that highlight the key contributions from original texts. this study introduces a novel method leveraging deep learning, specifically the sBERT model to summarize scientific documents. the proposed approach treats the extractive summarization as a classification problem using a dual BERT model setup. the methodology is evaluated using data set from CL-SciSumm. Results indicate that our approach significantly outperforms the existing methods in terms of ROUGE scores, demonstrating its effectiveness in generating accurate summaries of scientific literature.

关键词： Citation Based Summarization Scientific Documentation Deep learning BERT and sBERT model Natural Language Processing

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Deep Reinforcement learning-based Real-time Online Energy Management Strategy for Electric Vehicle Charging Stations 4

Deep Reinforcement Learning-based Real-time Online Energy Ma...

引用

4th Power System and Green Energy conference (PSGEC)

作者： Yang, Zhaoqiang Li, Longtan Yao, Rui Liu, Chunxiu Liu, Yimin Zhou, Zaiyan Pingyuan Power Supply Co State Grid Shandong Elect Power Co Dezhou Peoples R China Dezhou Power Supply Co State Grid Shandong Elect Power Co Dezhou Peoples R China

ISBN: (纸本)9798350365573;9798350365580

With the current "dual-carbon" environment, the transform of transportation electrification has become an important way in the pursuit of the "dual-carbon" target, but the uncertainty of the arrival time and charging demand of electric vehicles (EV) has brought great challenges to the design of charging schemes for charging stations (CSs). To maximize the EV users' charging demand, the paper proposes a real-time online energy management strategy for CSs based on Deep-Reinforcement-learning (DRL). First, the control process for EV charging is a Markov decision process, and the Multi-Attention-Actor-Critic (MAAC) algorithm with concentrated training based on a decentralized execution framework is established using the CS as an intelligent. then, an energy management strategy is proposed to maximize the profit of the CS. Finally, the real-time online energy management results of CSs based on DRL are analyzed. the results show that the strategy proposed in this paper can effectively improve the profit of CSs under the assumption of meeting the charging needs of EV users.

关键词： charging control Deep-Reinforcement-learning electric vehicles energy management Markov decision process

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Learnscan: An Interactive learning Platform for Enhanced Education Using Augmented Reality 4

Learnscan: An Interactive Learning Platform for Enhanced Edu...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Kamath, Ashwini Deepikaa, T.R. Godlin Donya, S.P. Pavithra, R. Madhumathi, R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331529635

An Interactive learning Platform for Enhanced Education using Augumented Reality (AR) presents the development of an innovative educational website and app designed to enhance learning experiences through the integration of QR codes and Augmented Reality technology. the platform features a curated list of QR codes, each assigned to specific educational topics. Upon scanning a QR code, users are directed to a dedicated website showcasing a detailed 3D model related to the QR code's assigned subject matter. Scanning a QR code associated with 'heart' will lead to a 3D model of the human heart. the website offers an interactive interface that allows users to explore and manipulate the 3D models in real-time. Also, the platform incorporates AR functionality, enabling users to view and interact with the models in their physical environment. It is also aided with audio recordings and description related to the information which is provided in the website. this immersive approach aims to provide a more engaging and effective learning experience, especially for complex subjects where visualizations are beneficial. Key features of the platform include user-friendly navigation, highquality 3D models, and seamless integration with AR technology. © 2024 IEEE.

关键词： 3D modeling

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Reinforced integrated deep transfer learning network based on multi-fault domain transformer fault voiceprint recognition 4

Reinforced integrated deep transfer learning network based o...

引用

4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Mu, Mingliang Niu, Jianfei Guo, Guangqian Tian, Maoxiang Li, Jinlin Li, Jingyi State Grid Shandong Electric Power Company Binzhou Power Supply Company Binzhou China

ISBN: (纸本)9798331532765

Power transformers emit continuous vibration signals during operation. the signals contain a large number of pulses and fluctuations caused by mechanical faults. they are the main data source for evaluating the operating status of power transformers. In order to study the voiceprint characteristics of vibration acoustic signals of transformers under different operating conditions, a reinforced integrated deep transfer learning network (REDTLN) for multi-fault domain voiceprint recognition is proposed. First, use various new kernel maximum mean difference (kMMD) to build multiple deep transfer learning networks (DTLN) for individual origin single destination field adjustment, so that Make DTLN transferable features diversified. Additionally, a novel unified unsupervised ensemble learning is formulated according to maximum mean difference (MMD) and various metrics, and using this unified measure as motivation, A novel training approach is constructed to search for Efficacious combinations of these DTLNs Rules to achieve enhanced integration of multi-source deep transfer learning networks. Improved accuracy and robustness in dynamic noise environments and complex operating conditions. Empirical outcomes indicate that the suggested model can accurately determine the abnormal operating status of the transformer and diagnose six typical faults of the transformer in different noise environments, with a diagnosis accuracy of more than 99%. © 2024 IEEE.

关键词： Deep reinforcement learning

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Comparative Analysis of Machine learning Algorithms for Predicting Student Academic Performance in Higher Education 4

Comparative Analysis of Machine Learning Algorithms for Pred...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Cruz, Michael Marvin P. Lumauag, Ramil G. College of Computer Studies Central Philippines State University San Carlos Campus Negros Occidental San Carlos City Philippines Iloilo Science and Technology University Computer Department La Paz Iloilo City Philippines

ISBN: (纸本)9798331529635

In recent years, educational institutions have increasingly sought to leverage data-driven approaches to enhance student success and retention. While numerous machine learning algorithms have been created to forecast academic performance, their efficacy differs among datasets, domains, and institutional contexts. the optimal algorithm for particular settings in higher education remains uncertain. this study aims to compare multiple algorithms such as Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naive Bayes, and K-Nearest Neighbors to identify the most effective model for predicting student academic performance in higher education institutions (HEIs). this paper also introduces the application of SMOTE, which focuses on addressing class imbalance. Evaluation of multiple models comparing their performance across metrics such as accuracy, precision, recall, and F1-Score was present in this paper. the results of the comparative analysis showed that Random Forest outperforms other algorithms, particularly when SMOTE is applied, with an accuracy of 97%, demonstrating its robustness and adaptability in handling imbalanced datasets. this paper underscores the capacity of machine learning to furnish educational stakeholders with novel instruments for predicting student outcomes, facilitating prompt interventions to enhance academic performance. © 2024 IEEE.

关键词： Contrastive learning

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