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data Quality-Driven Top-k Rule Discovery 3rd

Data Quality-Driven Top-k Rule Discovery

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Han, Ziyan Chen, Wanjia Han, Yunpeng School of Computer Science and Engineering Beihang University Beijing China School of Mathematical Sciences Shenzhen University Shenzhen China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge United States

ISBN: (纸本)9789819616978

Existing works on rule discovery often yield an overwhelming number of rules with a high computational cost. Moreover, few studies consider the impact of data quality on the reliability of rules produced. This paper studies the top-k rule discovery problem that returns a set of top-k reliable rules. We introduce a novel scoring function that incorporates data quality, providing a more comprehensive and fine-grained assessment of rules. Based on this scoring function, we develop an efficient algorithm for discovering top-k high-quality rules in relational data. To enhance the performance of our algorithm, we propose effective pruning and optimization strategies that significantly reduce the search space and improve computational efficiency;additionally, we parallelize it for further acceleration. Through extensive experiments on real-life datasets, we verify the effectiveness and efficiency of the proposed method, confirming its practical utility in data quality-driven top-k rule discovery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data reliability

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machine-learning based Text Entity Exaction Method for Power System Transformer Maintenance 3

Machine-learning based Text Entity Exaction Method for Power...

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3rd international conference on Control Theory and Applications, ICoCTA 2023

作者： Fan, Jifeng Ji, Zhi Li Keqin Zhang, Yanbin Liang, Haiyang Zhang, Wei Zhang, Tianlong Co. Ltd. Wuhai China

ISBN: (纸本)9781839539824

Text mining of power system equipment has important applications in fault diagnosis. With the improvement of intelligence and informatization level of power system equipment, a large amount of device text data is recorded and stored, which contains rich fault information. Through text mining technology, valuable information can be extracted from these data for equipment fault diagnosis and prediction. This paper exploited various model parameter structures such as LSTM, universal BERT, power text BERT, and Improved BERT on text exaction performance in the context of power systems, and compared the performance differences between these models under small sample tasks. The solutions are evaluated through experiments and the numerical results are provided and discussed. © The Institution of Engineering & Technology 2023.

关键词： Failure analysis

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3rd international conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

3rd International Conference on "Emerging Trends and Technol...

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3rd international conference on "Emerging Trends and Technologies on Intelligent Systems" (ETTIS-2023)

ISBN: (纸本)9789819939626

The proceedings contain 56 papers. The special focus in this conference is on international conference on "Emerging Trends and Technologies on Intelligent Systems". The topics include: Multi-step Online Hate Speech Detection and Classification Using Sentiment and Sarcasm Features;EEG-Based Real-Time Prediction of Cognitive State on Smartphone;comparative Study in Parkinson’s Disease Diagnosis Using machine learning;ioT-Based Monitoring System for Hydroponics: Design and Implementation;prediction of Lattice Constant of Pyrochlore Compounds Using Optimized machine learning Model;an Approach for Efficient Graph mining from Big data Using Spark;new Algorithm to Prevent Online Test Fraud Based on Cognitive Services and Input Devices Events;comparison of Sentinel and Landsat data Sets over Lucknow Region Using Gradient Tree Boost Supervised Classifier;Evaluation Performance and Routing on VANET Architecture: A Narrative Review;WORK-PERF: An Intelligent Predictive Model for Work Performance Rating;Touch Pointer Movement-Based PIN Entry in Smartphones to Assist Persons with Visual Impairments;A Novel Approach for Detection of Soybean Leaf Disease Using Bayesian Optimized-KNN Classifier with NCA-Based Feature Selection;Design and Analysis of a Dual-Band Quad-Ridge Horn Antenna Using 3D Printing and PLA for Wireless Communication Enhancement Performance;Identify the Need for Rainwater Harvesting System (RWHS) in India Using Rainfall data Analysis;Solid Waste Identification and Classification Method Based on Feature Selection and Hybrid ResNet CNN Models in Smart Environment;machine learning Framework for Stress Identification of Humans;survey Paper on Detection of Water Bodies in Satellite Imagery;decentralized Market for Spices Using Hyperledger;analyzing the Involvement of Various Similarity Measures in Trust and Distrust-Based Recommender System;XAI-Based Light-Weight CNN-HAR Model Using Random Sampling.

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Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task 26

Improving End-to-End Text Image Translation From the Auxilia...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - Theory and Applications (IMTA)

作者： Ma, Cong Zhang, Yaping Tu, Mei Han, Xu Wu, Linghui Zhao, Yang Zhou, Yu Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR 95 Zhongguan East Rd Beijing 100190 Peoples R China Zhongke Fanyu Technol Co Ltd Fanyu AI Lab Beijing 100190 Peoples R China Samsung Res China Beijing SRC B Beijing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research. However, data sparsity limits the performance of end-to-end text image translation. Multi-task learning is a non-trivial way to alleviate this problem via exploring knowledge from complementary related tasks. In this paper, we propose a novel text translation enhanced text image translation, which trains the end-to-end model with text translation as an auxiliary task. By sharing model parameters and multi-task training, our model is able to take full advantage of easily-available large-scale text parallel corpus. Extensive experimental results show our proposed method outperforms existing end-to-end methods, and the joint multi-task learning with both text translation and recognition tasks achieves better results, proving translation and recognition auxiliary tasks are complementary. (1)

关键词： Training Image recognition Text recognition Optical character recognition Multitasking Decoding machine translation

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MVCSR: Multi-view Contrastive Sequential Recommendation 3rd

MVCSR: Multi-view Contrastive Sequential Recommendation

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Lee, Kai Wu, Xuan Zhao, Junfeng Yu, Chenyun Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China

ISBN: (纸本)9789819624676

Contrastive learning technology has shown significant potential in alleviating the data sparsity problem in sequential recommendation. Nevertheless, existing methods mainly derive self-supervised signals from a single perspective or rely on abundant domain-specific auxiliary information to extract high-quality user representations, which either fail to make the most of supervised information or lack of flexibility to extend to more scenarios. In this paper, we propose a novel Multi-View Contrastive Sequential Recommendation (MVCSR) method for sequential recommendation scenarios with few auxiliary information. MVCSR utilizes the set-level, sequence-level and preference-level user information to derive valuable supervised signals and provides three effective contrastive learning strategies to improve the item representations, sequence embeddings and user preferences, eventually boosting the recommendation performance. Experimental results reported from four Amazon datasets have demonstrated the superiority of MVCSR compared with all the state-of-the-art sequential recommendation baselines. Moreover, we validate the effectiveness of each contrastive learning strategy and show our advantage at dealing with the recommendation for users with short sequences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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Human Posture Detection on Lightweight DCNN and SVM in a Digitalized Healthcare System 3

Human Posture Detection on Lightweight DCNN and SVM in a Dig...

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3rd international conference on Applied Artificial Intelligence (ICAPAI) - Human-Centric AI for the Digital Society

作者： Ogundokun, Roseline Oluwaseun Maskeliunas, Rytis Damasevicius, Robertas Kaunas Univ Technol Dept Multimedia Engn Kaunas Lithuania Landmark Univ Omu Aran Dept Comp Sci Omu Aran Nigeria Landmark Univ Omu Aran SDG 11 Sustainable Cities & Commun Omu Aran Nigeria Kaunas Univ Technol Fac Informat Kaunas Lithuania

ISBN: (纸本)9798350328929

The domains of Human Posture (HP) and artificial intelligence (AI) have placed significant emphasis on Human Posture Classification (HPC). By recognizing the standing, sitting, and walking positions of aged people, HPC could effectively monitor their health condition and ensure their well-being. Detection of various postures is difficult owing to the need for enough datasets and a classification framework. MobileNet is a lightweight Deep Convolution Neural Network (DCNN) with a better recognition rate and fewer parameters. To further increase the generalization ability while reducing the number of models hyperparameters, techniques such as regularization, Transfer learning (TL) and neural architecture search can be employed. To address the issue of a shortage of annotated data and improve approach effectiveness, TL and data Augmentation (DA) are utilized for MobileNet and Xception in this study. The Support Vector machine (SVM) model boosts performance instead of the final Fully Connected Layer (FCL). To facilitate the effectiveness of our approach, TL and DA are both used for SVM. The investigation associated the efficacy of the suggested approach with that of other cutting-edge image classification techniques, including ResNet50V2, InceptionV3, and DenseNet201, and found that the presented methodology is better. Our recommended method captures temporal and depth characteristics from the image separately and incorporates them into classification computations. The suggested approach founded on MobileNet hybridized with SVM attains the top performance with 92.12% test accuracy (Acc), 95 % area under the curve (AUC), 92% recall (Rec), 93% precision (Prec), 92% F1 score and a computational period of 3974secs.

关键词： Human Posture detection Deep learning Transfer learning Image classification Human Posture Artificial Intelligence

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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Design and Implementation of Deep learning Based Illicit Drug Supplier Detection System 3rd

Design and Implementation of Deep Learning Based Illicit Dru...

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3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

作者： Arulmozhi, M. Iyer, Nandini G. Amutha, C. Sophia, S. Jeny Sivakumar, P. Nivethitha, S.B. Rajalakshmi Engineering College Chennai602105 India

ISBN: (纸本)9789811959356

Video-based face recognition is likely exigent for the reason that a large amount of data has to be processed and it involves intricate computations. This paper provides a novel video-based face recognition system, designed using the convolution neural network (CNN) algorithm. The CNN is trained with the database generated from a video stream to recognize the drug dealers. If a drug dealer is detected, the system alerts the school or college management. The proposed drug distributor detection system is implemented using a 64-bit raspberry Pi 3 microcontroller and a Pi camera module that captures the video stream in real-time. If the detected face is a drug dealer, the system alerts the school or college management through SMS. The experimental results show that the CNN is capable to classify authorized students and drug dealers correctly and detect abnormal activities without using huge amount of training data. A top recognition accuracy of 99.58% was acquired for CNN. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Plant Disease Detection Using machine learning Techniques 3

Plant Disease Detection Using Machine Learning Techniques

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3rd international conference for Emerging Technology, INCET 2022

作者： Varshney, Divyanshu Babukhanwala, Burhanuddin Khan, Javed Saxena, Deepika Singh, Ashutosh Kumar Nit Kurukshetra Department of Computer Application Haryana India

ISBN: (纸本)9781665494991

Plant diseases may have a major impact on food safety, also a considerable decline in agricultural product output. The great majority of automated systems developed thus far are based on digital pictures, allowing for the rapid deployment of algorithms. The difficulty of autonomous illness identification in plants has been solved using traditional machine learning approaches such as (SVM) support vector machines, Multilayer Perception Neural Networks, and Decision Trees. The focus of this article was on leaf plant disease. A new plant leaf disease detection technique has been developed that is based on a transfer learning methodology such as deep learning, where CNN is employed as a feature extractor and SVM is used for classification. A benchmark dataset called PlantVillage was used to assess the evaluation of the proposed model. The suggested model was examined and compared to current methodologies, and it outperformed previous work, achieving an 88.77 percent training accuracy. © 2022 IEEE.

关键词： data mining

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Efficiency Improvement of Deepening data Application of State Grid Power under machine learning Algorithm

Efficiency Improvement of Deepening Data Application of Stat...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Wei Xing Tong Wu Botao Wu State Grid Customer Service Center DongliTianjin China

In the process of deepening the data processing of State Grid Power, a machine learning algorithm is proposed to solve the problem that the data efficiency of State Grid Power cannot be effectively improved. Using mechanical learning theory to classify the power data of State Grid and find mining strategies for all kinds of data; Then, under the guidance of machine learning theory, we find the best data efficiency improvement strategy. The results show that the machine learning algorithm can improve the accuracy of data processing and shorten the time of data deepening.

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