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Adaptive Bias-Aware Feature Generation for Generalized Zero-Shot Learning

ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 280-290页

作者： Yang, Yanhua Zhang, Xiaozhe Yang, Muli Deng, Cheng Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Zero-Shot Learning (ZSL) aims to recognize unseen classes that never appear during training. Recently, generative adversarial networks (GANs) have been introduced to convert ZSL into a supervised learning problem by synthesizing unseen visual features. However, since unseen classes are never experienced for the generator during training, the synthesized unseen visual features often become heavily biased towards seen classes, or sometimes there is even no meaningful class that can be assigned to them. This is known as the bias problem. In this paper, we propose a novel method, namely Adaptive Bias-Aware GAN (ABA-GAN), to alleviate generating biased visual features. For this purpose, we build a semantic adversarial network to regularize the feature generator. Specifically, an adaptive adversarial loss is proposed to constrain the feature distributions, which avoids the generation of meaningless visual features. Meanwhile, a domain divider is presented to explicitly distinguish synthesized visual features between seen and unseen domains, such that the bias towards seen classes can be alleviated. Moreover, we propose a novel metric named bias score (BS) to explicitly quantify the degree of the strong bias. Extensive experiments on four widely used benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art approaches under both ZSL and GZSL protocols.

关键词： visualization Semantics Generators Training Generative adversarial networks data models Benchmark testing Zero-shot learning generative adversarial network image classification bias problem

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A Scientometric Analysis of the Capacity Expansion and Planning Research

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ieee TRANSACTIONS ON ENGINEERING MANAGEMENT 2023年 71卷 6382-6405页

作者： AlDurgam, Mohammad M. Tuffaha, Firas M. Abdel-Aal, Mohammad A. M. Almoghathawi, Yasser Saleh, Haitham H. Saleh, Zead King Fahd Univ Petr & Minerals Dept Ind & Syst Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Construct Engn & Management Dhahran 31261 Saudi Arabia

Numerous technoeconomic factors have had a considerable impact on the industrial and service sectors. The best uses of production and service resources have been a main research area. Capacity planning, which aims at the optimum utilization of resources, has attracted a lot of scholarly attention. To the best of our knowledge, however, there has never been a scientometric analysis published on this subject;as a result, the goal of this article is to provide the first such analysis. The goal of this study is to gain a thorough structural view of the background and important facets of capacity expansion and planning research. The Web of science database, VOSviewer, and CiteSpace software are used to give an overview. Our research indicates that after the year 2000, the publication rate increased significantly. Applications for capacity planning can be divided into eight main groups based on the authors' keyword analysis. We also list both well-known and underutilized modeling and solution methods. Recent applications, research trends, and some omissions are highlighted in this study. Leading contributing countries, institutions, and the most common research outlets are mapped and analyzed. Finally, some potential future research topics are suggested

关键词： Capacity planning Bibliometrics Planning data visualization Market research databases Surveys Bibliometric analysis capacity expansion Index Terms capacity planning scientometric analysis

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 ieee.

关键词： Support vector machines

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A MULTI-STREAM NETWORK FOR MESH DENOISING VIA GRAPH NEURAL NETWORKS WITH GAUSSIAN CURVATURE 30

A MULTI-STREAM NETWORK FOR MESH DENOISING VIA GRAPH NEURAL N...

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30th ieee International Conference on Image Processing (ICIP)

作者： Zhao, Zhibo Wu, Wenhui Liu, Hongjie Gong, Yuanhao Shenzhen Univ Shenzhen Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Reexen Technol Ltd Shenzhen Peoples R China Guangdong HK Joint Lab Big Data Imaging & Commun Shenzhen Peoples R China

ISBN: (纸本)9781728198354

3D meshes are getting popular in both research and industry. However, the meshes obtained via the 3D scanning equipment frequently contain a high level of noise. In this paper, we present a Gaussian Curvature Driven Multi-stream Network (GCM-Net) based on graph convolutional networks. This network can remove the noise while preserving the essential features during the 3D mesh denoising process. Our method is the first attempt to apply the high-order feature (i.e., Gaussian curvature) in the denoising task, which is more descriptive for the shape of the mesh. GCM-Net consists of curvature stream, vertex stream, and face normal stream, where the curvature stream focuses on the high-order Gaussian curvature feature of 3D mesh. Our method achieves state-of-the-art results on a publicly available dataset, demonstrating its effectiveness. The proposed method can be applied in various applications, such as 3D human body modeling, metaverse, object tracking and biomedical visualization.

关键词： Mesh denoising graph convolutional networks multi-stream Gaussian curvature

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Automobile Price Prediction using Machine Learning with data visualization 14

Automobile Price Prediction using Machine Learning with Data...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nishitha, U. Kandimalla, Revanth Mourya Vardhan Reddy, M. Jyotsna, C. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350335095

The automobile industry is experiencing a rapid increase in demand due to the growing population, leading to an increase in the sales of automobiles. Industry experts focus on renewing automobiles and increasing their efficiency and power while making them user-friendly and cost-effective. To assist people in purchasing cars within their budget, machine learning algorithms are utilized to predict the price of cars accurately. A machine learning model with high accuracy is integrated into a user-friendly interface, allowing people to view the precise price of the car. The user interface is designed to be user-friendly, making it easy for people to use. This paper presents various regression and classification models and their accuracies and promises to provide the price of the cars according to given inputs with a user-friendly interface. A supervised regression model is developed using multiple regression techniques trained from the car dataset provided. data cleaning is performed to enhance the performance of these regression models by converting null values to non null values in the dataset. © 2023 ieee.

关键词： Machine learning

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Design and Implementation of a Cost-Effective IoT-Enabled Multi-Parameter System for Water Quality Monitoring in Aquaculture and Aquaponics 2

Design and Implementation of a Cost-Effective IoT-Enabled Mu...

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2nd International Conference on Electronics, Energy and Measurement, IC2EM 2023

作者： Ghobrini, Abdelmalek Belhakimi, Abdessamed Remram, Youcef University of Science and Technology Houari Boumediene Electronic Instrumentation Laboratory Alger Algeria

ISBN: (纸本)9798350314243

This paper details a cost-effective IoT-based system that monitors water quality in aquaculture and aquaponics. The system uses low-cost probes equipped with a conditioning module to measure pH, conductivity, total dissolved solids (TDS), turbidity, and temperature. Automatic temperature compensation is incorporated for pH and TDS measurements. The data processing and communication are performed using an ESP8266 NodeMCU, which connects to a Firebase database through a Wi-Fi connection. The collected data can be accessed by network users via an IP address, enabling real-time monitoring. The system features an OLED screen for parameter visualization, an ADS1115 ADC for accurate sensor measurements, and a 3D-printed case for housing the components. The system also includes automatic calibration functionality using standard calibration solutions. This paper demonstrates the feasibility and effectiveness of an affordable solution for continuous water quality monitoring in aquaculture and aquaponics systems and provides metrology for proper sensor integration and calibration. © 2023 ieee.

关键词： Aquaculture

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Annotation of International Chinese Classroom Components Based on Large Language Model 13

Annotation of International Chinese Classroom Components Bas...

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13th International Conference on Educational and Information Technology, ICEIT 2024

作者： Chen, Siting Cao, Ni Wang, Ziyu Li, Jimei School of Information Science Beijing Language and Culture University Beijing China

ISBN: (纸本)9798350372663

The paper aims to explore the differences in the field of smart education by comparing and analyzing the annotation results of international Chinese classroom data. Applying Professor Yanqun Zheng's International Chinese Language Teaching Components Model, further annotation work was carried out based on the research of its structure and process. First, we carefully annotated the data from the international Chinese education classroom of Beijing Language and Culture University. Then we explored the differences in the application of Large Language Models (LLMs) in the field of education through the results of manual annotation. The experimental results show that the Annotation of SparkDesk is more consistent with the results annotated by people. It may contribute to the development of LLMs in educational classroom data annotation and analysis, as well as their application in the field of education. © 2024 ieee.

关键词： Computational linguistics

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WoS Driven Bibliometric Analysis on Genetic Disease Prediction Using Artificial Intelligence

WoS Driven Bibliometric Analysis on Genetic Disease Predicti...

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2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Kaur, Geet Kiran Manchanda, Nandita Singla, Sanjay Modi, Soham Kr Rana, Pratham Singh Sihag, Daulat Chandigarh Univerity Department of Computer Science Engineering Punjab India Chandigarh University Cse Punjab India

ISBN: (纸本)9798350380880

The term 'artificial intelligence' (AI) is used to describe the practice of simulating human intelligence in a machine. Robotics, medical diagnostics, medical statistics, and even human biology are just a few of the applications of artificial intelligence. This article aims to provide a bibliometric analysis of artificial intelligence's potential applications in predicting genetic diseases from 2018 to 2023. The primary objective of this research is to evaluate the available data on the efficacy of using AI for the prevention of hereditary illnesses. The study's authors acknowledged and affirmed the issue's significance and the topic's potential to aid future academics in achieving strategic alignment between information and technology. There have been a total of 127 articles on this subject, all of which have been analyzed using the Web of science bibliometric technique. Articles, conference papers, and book chapters all contribute to this bibliometric study, which is used to gauge the worldwide influence of publications in a certain field. This article reviews the best practices for doing and using bibliometric research using the R software package. In the realm of genetic disease prediction, visualization makes it simple to view and comprehend various approaches. © 2023 ieee.

关键词： Artificial intelligence

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Self-Supervised Learning for Heterogeneous Audiovisual Scene Analysis

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 3534-3545页

作者： Hu, Di Wang, Zheng Nie, Feiping Wang, Rong Li, Xuelong Renmin Univ China Beijing Key Lab Big Data Management & Anal Method Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 10698 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China

Due to the difficulty of annotating large amounts of training data, directly learning the association of sound and its makers in natural videos is a challenging task for machines. In this paper, we present a novel audiovisual model that introduces a soft-clustering module as the audio and visual content detector, and regards the pervasive property of audiovisual concurrency as the latent supervision for inferring the correlation among detected contents. Furthermore, we discover for the first time that the complexity of data has an impact on the training efficiency and subsequent performance of audiovisual model, i.e., more complex data brings more obstacles to the model training, and degrades the performance of downstream audiovisual tasks. To address the issue of audiovisual learning, we propose a novel heterogeneous audiovisual scene analysis module that trains the model from simple to complex scene. We show that such ordered learning procedure rewards the model the merits of easy training and fast convergence. Meanwhile, our audiovisual model can also provide effective unimodal representation and cross-modal alignment performance. We further deploy the well-trained model into practical audiovisual sound localization and separation tasks. We show that our localization model significantly outperforms existing methods, based on which we show comparable performance in sound separation task by comparison to several related SOTA audiovisual learning methods without referring external visualsupervision.

关键词： visualization Task analysis Location awareness Complexity theory Training Concurrent computing Image analysis Audiovisual sound localization and separation heterogenous audiovisual scene analysis multimodal audiovisual learning

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Traffic Congestion Classification Using Convolutional Neural Networks: A Simplified Approach to Traffic Management 6

Traffic Congestion Classification Using Convolutional Neural...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Danendra, Alfito Devin Kaurow, Joaquin Ezra Osvaldo, Nelson Pratama, Galih Dea Hasana, Silviya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

With the widespread success of the automotive industry;one major issue that developing countries like Indonesia face is traffic congestion. This problem amplifies many issues in modern society including traffic accidents, air pollution, and also economic development. Traditional traffic management approaches have proven to be insufficient in addressing these challenges effectively. Therefore, this paper proposes a novel approach for traffic management using object detection with Convolutional Neural Network (CNN). The paper presents an insightful visualization of class occurrences, highlighting the distribution of congested and uncongested traffic images across train, test, and validation datasets. Through data augmentation techniques and hyperparameter tuning using a Keras tuner, the CNN model is further enhanced, resulting in improved performance. The retrained model achieves a high accuracy level of 95.92% on the test dataset, consolidating its effectiveness in traffic management. This research offers a promising solution to alleviate traffic congestion. © 2023 ieee.

关键词： Convolutional neural networks

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