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arXiv 2024年

作者： Jiang, Lu Wang, Qi Chang, Yuhang Song, Jianing Fu, Haoyue Yang, Xiaochun College of Sciences Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China Software College Northeastern University Shenyang110819 China School of Computer Science and Engineering Northeastern University Shenyang110819 China

Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction. The traditional machine learning method tends to favor the majority class, which leads to the lack of minority class information in the model. Moreover, most existing models will produce abnormal sensitivity issues or performance degradation. We propose a robust learning algorithm based on adaptive cost-sensitivity and recursive denoising, which is a generalized framework and can be incorporated into most stochastic optimization algorithms. The proposed method uses the dynamic kernel distance optimization model between the sample and the decision boundary, which makes full use of the sample’s prior information. In addition, we also put forward an effective method to filter noise, the main idea of which is to judge the noise by finding the nearest neighbors of the minority class. In order to evaluate the strength of the proposed method, we not only carry out experiments on standard datasets but also apply it to emotional classification problems with different imbalance rates (IR). Experimental results show that the proposed general framework is superior to traditional methods in Accuracy, G-mean, Recall and F1-score. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Sentiment Analysis in Social Media Using Deep Learning Techniques

Sentiment Analysis in Social Media Using Deep Learning Techn...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Babu Pravin Vishnu Shinde Neha Agrawal M. Vijaya Bhaskar K. Hemabala Sweta Priya Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Department of Artificial Intelligence & Data Science Shah and Anchor Kutchhi Engineering College Affiliated to University of Mumbai Maharashtra India Department of IT Maharaja Agrasen Institute of Technology New Delhi Department of Computer Science &Engineering SriVenkateswara Institute of Technology Anantapur Andhra Pradesh India Department of Freshmen Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tiruvathi Department of Amity School of Communication University Amity University Patna

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This work offers a thorough investigation of sentiment analysis in social media using deep learning techniques. Understanding stoner sentiment has become essential for colourful operations like marketing, public opinion research, and recommendation algorithms due to the rapid expansion of social media networks. Deep learning approaches have emerged as crucial tools for sentiment analysis tasks because of their capacity to extract intricate patterns from massive datasets. The usefulness of deep learning models, such as convolutional neural networks (RNNs), and intermittent neural networks (RNNs), and their variations, in extracting complex sentiment information from social media textbooks is investigated in this study. Additionally, the study looks at model infrastructures, point representations, and pre-processing techniques to improve sentiment analysis performance. The results show how effective deep learning techniques are in extracting sentiment directly from various social media posts, opening the door for more sophisticated sentiment-apprehensive digital processes

关键词： Deep learning Sentiment analysis Analytical models Adaptation models Text analysis Social networking (online) Neural networks Convolutional neural networks Research and development Videos

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Internet of Things (IoT) Feedback System using Raspberry Pi

Internet of Things (IoT) Feedback System using Raspberry Pi

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Pradeepkumar G Ramraj B Manimuthu Ayyannan Rohith Bhat C Kalavathi Devi T Neelam Sanjeev Kumar Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electrical and Electronics Engineering Nandha Engineering College Erode India AI/ML and Data Expert San Francisco Bay Area California USA Institute of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electronics and Instrumentation Engineering Kongu Engineering College Erode Tamil nadu India Department of Biomedical Engineering Saveetha School of Engineering Chennai India

Constant monitoring and examination of customer input in modern businesses, public spaces, and institutional settings necessitates a significant investment in record-keeping equipment. This study includes a feedback mechanism to help with system documentation and maintenance. In the context of this work, “feedback” refers to the response of the surrounding environment to a specific activity. This study tracks the client reviews by physically rating them and then uploading the results to the cloud. Anyone with access can see how most of the comments were received. With this system in place, monitoring the status of each item of input is a breeze.

关键词： Industries Documentation Organizations Maintenance engineering Logic gates Software Product development

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Personalized Recommendation Based on Entity Attributes and Graph Features 12

Personalized Recommendation Based on Entity Attributes and G...

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12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Zhu, Yi Dong, Bingbing Sha, Zhiqing Yangzhou University School of Information Engineering Yangzhou China Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Institute of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods. © 2021 IEEE.

关键词： Websites

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Icon-to-Icon Translation using Pixel-Level Weight Adversarial Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 147-160页

作者： Pan, Yao-Ming Lin, Kai-Biao Chen, Chin-Ling School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Provincial University Putian351100 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung41349 Taiwan

We investigate image-to-image translation as a solution to icon-to-icon translation problems. Icon-to-icon translation aims to translate the input icon to appear like the target domain’s icons and simultaneously preserve the original icon’s information (e.g., application logo, text and background color). We present a novel approach to trans-lating icons to the version with the target style intelligently. By observing many icons with various styles, we propose a pixel-level weight matrix to calculating the weighted losses. These losses guide the generator to focus on the critical regions of the icon. People can clearly get the original icon’s information from the proposed method’s results, while the existing state-of-the-art image-to-image translation methods may fail to do this. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Pixels

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Three-Dimensional Convolutional Neural Network for Wheat Rust Disease Classification

Three-Dimensional Convolutional Neural Network for Wheat Rus...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： N Jagadeeshan Y Nagendar Swarnalatha K Kassem Al-Attabi Ramachandra A C Electronics and Communication Engineering Department Mallareddy Engineering College for Women (Autonomous) Hyderabad Telangana India School of Computer Science and Engineering SR University Warangal Telangana India Department of Artificial intelligence and Data Science Maharaja Institute of Technology Thandavapura India The Islamic university Najaf Iraq Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79 % when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet.

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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

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Science China(Information Sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph ${\cal C}{\cal O}{\cal N}{\cal G}{\cal E}{\cal S}{\cal T}$ model communication complexity round complexity

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Dynamic Exit Selection and Counting Area Adjustment for Efficient Crowd Evacuation in Asymmetrical Layouts

Dynamic Exit Selection and Counting Area Adjustment for Effi...

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Information Technology (ACIT)

作者： Omar Khair Alla Alidmat Raed Alazaidah Mohammad Arabiat Hayel Khafajeh Mohammad Aljaidi Umi Kalsom Yusof Ali Elrashidi Computer Science Department Zarqa University Zarqa Jordan Data Science and Artifical Inteligence Department Zarqa University Zarqa Jordan Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

This research introduces an innovative methodology to address the significant challenge of evacuating large crowds from buildings with multiple exits, particularly in scenarios with asymmetrical exit configurations and high population densities. The proposed model incorporates dynamic counting areas that adjust according to evacuee density, alongside a probabilistic exit selection mechanism influenced by enhanced dynamic parameters—congestion, distance, and empty. It also accounts for heterogeneous agent behaviors to more accurately represent evacuee dynamics in complex, asymmetrical environments with dense crowds. Key findings indicate that the model effectively alleviates bottlenecks at congested exits, optimizes exit usage, and enhances overall evacuation safety. Comparative analyses demonstrate that the proposed model significantly improves evacuation efficiency and reduces travel distances compared to previous models. Simulation results indicate that agent density directly affects evacuation time, with lower congestion levels facilitating faster egress. These advancements lead to more precise evacuation time predictions and crowd flow assessments, offering valuable insights for refining emergency egress strategies in complex settings. Future research will focus on refining the model with real-world data to expand its applicability in diverse settings, such as stadiums and public transportation hubs, thereby enhancing emergency preparedness and response.

关键词： Analytical models Simulation Refining Layout Probabilistic logic data models Safety Information technology Public transportation Population density

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UniFace: Unified Cross-Entropy Loss for Deep Face Recognition

UniFace: Unified Cross-Entropy Loss for Deep Face Recognitio...

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International Conference on computer Vision (ICCV)

作者： Jiancan Zhou Xi Jia Qiufu Li Linlin Shen Jinming Duan National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Computer Vision Institute Shenzhen University Aqara Lumi United Technology Co. Ltd. School of Computer Science University of Birmingham UK Alan Turing Institute UK

As a widely used loss function in deep face recognition, the softmax loss cannot guarantee that the minimum positive sample-to-class similarity is larger than the maximum negative sample-to-class similarity. As a result, no unified threshold is available to separate positive sample-to-class pairs from negative sample-to-class pairs. To bridge this gap, we design a UCE (Unified Cross-Entropy) loss for face recognition model training, which is built on the vital constraint that all the positive sample-to-class similarities shall be larger than the negative ones. Our UCE loss can be integrated with margins for a further performance boost. The face recognition model trained with the proposed UCE loss, UniFace, was intensively evaluated using a number of popular public datasets like MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace. Experimental results show that our approach outperforms SOTA methods like SphereFace, CosFace, ArcFace, Partial FC, etc. Especially, till the submission of this work (Mar. 8, 2023), the proposed UniFace achieves the highest TAR@MR-All on the academic track of the MFR-ongoing challenge. $\color{Blue}{\mathbf{Code}}$ is publicly available.

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Predicting Fall Events by a Spatio-Temporal Topological Network with Multiple Wearable Sensors

Predicting Fall Events by a Spatio-Temporal Topological Netw...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaohu Li Jiawei Liu Guorui Liao Mingrui Yin Shu Wang Guoxin Su Jun Liao Li Liu School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Aerospace Rocket Electronic Technology Co. Ltd Chongqing China College of Computer Science & Technology Zhejiang University Zhejiang China School of Materials & Energy Southwest University Chongqing China School of Computing and Information Technology University of Wollongong NSW Australia

A key challenge in sensor-based fall prediction is the fact that a fall event can often occur in various configurations of fall poses together with their own spatio-temporal dependencies. This leads us to define a spatio-temporal model to explicitly characterize these internal configurations of poses. In particular, we introduce a graph neural network with spatio-temporal topological structure to encode such latent relations among poses by capturing representative patterns in fall events. Moreover, a human body orientation estimator is devised to capture human low limbs information, and as a result, separate pose dependencies are globally consistent. Empirical evaluations on two benchmark datasets and one in-house dataset suggest our approach significantly outperforms the state-of-the-art methods.

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