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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3564-3578页

作者： Xu, Honghui Fang, Chuangjie Wang, Renfang Chen, Shengyong Zheng, Jianwei Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Zhejiang Wanli University College of Big Data and Software Engineering Ningbo315200 China Tianjin University of Technology College of Computer Sciences and Engineering Tianjin300384 China

Recently, tensor singular value decomposition (TSVD) within high-order (Ho) algebra framework has shed new light on tensor robust principal component analysis (TRPCA) problem. However, HoTSVD lacks flexibility in handling the hidden correlations along different modes of large-scale multidimensional data. Moreover, the utilization of fixed or data-independent transformations in HoTSVD may result in suboptimality. For a relief, we propose a dual-enhanced self-learning TSVD along all modes to address computational flaws and learn a lossless transformation that induces a lower average-rank tensor. Specifically, we multiply the learnable semiorthogonal matrices obtained through Tucker compression with the original tensor along all modes, thus obtaining a core tensor with more inherent low rankness. Building upon this foundation, a new TNN is introduced by generalizing HoTSVD to Mode-k TSVD, followed by the facilitation to the core tensor, achieving dual-enhancement. Moreover, a reweighting scheme is imposed on the Mode-k HoTSVD to learn the global low-rank correlation and provide an efficient numerical solution. Finally, an alternating direction method of multipliers (ADMM)-based algorithm is developed as a solver. Experimental results on several types of multidimensional visual data, including light field images (LFI) and color videos, demonstrate the superiority of the proposal over previous state-of-the-art methods. © 2020 IEEE.

关键词： Discrete Fourier transforms

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Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第8期47卷 77-88页

作者： Jasim, Hend Muslim Ghrabat, Mudhafar Jalil Jassim Abdulrahman, Luqman Qader Nyangaresi, Vincent Omollo Ma, Junchao Abduljabbar, Zaid Ameen Abduljaleel, Iman Qays Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Iraqi Commission for Computers and Informatics The Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Science Department Al-Turath University College Baghdad10013 Iraq College of Health Science Hawler Medical University Erbil Iraq Jaramogi Oginga Odinga University of Science and Technology Bondo40601 Kenya College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Technical Computer Engineering Department AL-Kunooze University College Basrah6100 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China Department of Computer Science College of Computer Science and Information Technology University of Basrah Basrah61004 Iraq

One of the segmentation techniques with the greatest degree of success used in numerous recent applications is multi-level thresholding. The selection of appropriate threshold values presents difficulties for traditional methods, however, and, as a result, techniques have been developed to address these difficulties multidimensionally. Such approaches have been shown to be an efficient way of identifying the areas affected in multi-cancer cases in order to define the treatment area. Multi-cancer methods that facilitate a certain degree of competence are thus required. This study tested storing MRI brain scans in a multidimensional image database, which is a significant departure from past studies, as a way to improve the efficacy, efficiency, and sensitivity of cancer detection. The evaluation findings offered success rates for cancer diagnoses of 99.08%, 99.87%, 94%;97.08%, 98.3%, and 93.38% sensitivity;the success rates of in particular were 99.99%;98.23%, 99.53%, and 99.98%. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Image segmentation

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IoT-Enhanced Agricultural Water Management System Utilizing Conventional Machine Learning Methods

IoT-Enhanced Agricultural Water Management System Utilizing ...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Singh, Jagendra Garg, Neha Kukreja, Sonal Saini, Mayank Singh, Ishaan Mall, Shachi Bennett University School of Computer Science Engineering and Technology Greater Noida India Publicis Sapient Manager Data Science Greater Noida India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350393415

This study delves into IoT-enhanced Agricultural Water Management, aspiring to transform farming for enhanced crop output and sustainability. It focuses on an intelligent system incorporating a sensor network and varied machine learning methods for climate monitoring and yield prediction in tomato cultivation. Notably, the Convolutional Neural Network (CNN) stood out with a 95.2% accuracy rate in predicting yields, highlighting its potential as a pivotal tool in effective irrigation management. This adaptive, data-driven system promises advanced water management, addressing pressing global concerns of food security and resource conservation by aligning agricultural practices with environmental parameters and enabling heightened crop productivity. © 2023 IEEE.

关键词： Water management

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Toward Responsible Artificial Intelligence Systems: Safety and Trustworthiness 8th

Toward Responsible Artificial Intelligence Systems: Safety a...

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8th International Conference on Engineering of computer-Based Systems, ECBS 2023

作者： Herrera, Francisco Department Computer Sciences and Artificial Intelligence Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada Spain

ISBN: (纸本)9783031492518

This short paper associated to the invited lectures introduces two key concepts essential to artificial intelligence (AI), the area of trustworthy AI and the concept of responsible AI systems, fundamental to understand the technological, ethical and legal context of the current framework of debate and regulation of AI. The aim is to understand their dimension and their interrelation with the rest of the elements involved in the regulation and auditability of AI algorithms in order to achieve safe and trusted AI. We highlight concepts in bold in order to fix the moment when they are described in context. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Hybrid RNN Based Text Classification Model for Unstructured data

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SN computer science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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Face Recognition Based on SRCS Algorithm and Score of Exponential Weighting

Face Recognition Based on SRCS Algorithm and Score of Expone...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2023

作者： Zhang, Xuexue Zhang, Yongjun Gao, Weihao Long, Wei Yao, He Sate Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9789819708543

Face recognition is a prominent research area within computer vision, offering significant value across various fields of life, including certificate verification, security systems, and human-computer interactions. Nonetheless, enhancing the reliability and precision of face recognition systems is persistently challenging due to variables like facial expressions, various scenes, and occlusion. This paper introduces an enhanced SRCS (Sum of Representation Coefficients as Score) algorithm, which builds upon the two-stage sparse representation approach. The underlying principle is that a higher representation value of each class of training images vis-à-vis test images suggests a stronger correlation between the test image and the respective class. Consequently, the algorithm computes the sum of representation coefficients for the training images to ascertain the distances between the classes and the test images. Following this, it identifies classes with high scores as prospective representatives of the test samples. Moreover, the setting of reasonable weights in the weighted fusion of addition weighting, based on the TSFR (The Two-Step Face Recognition) method, presents challenges, and varying training samples require different optimal weights. Therefore, this paper proposes an exponential weighting method score that obviates the need for setting any weights while effectively enhancing recognition accuracy. This proposed method undergoes rigorous validation through comprehensive experiments conducted on renowned databases such as FERET, PRL, YALE, and GT, with the results consistently affirming the effectiveness of the method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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SafeRoute: Pioneering Online Ride Booking with Pega & ML-Powered Safety Measures 8

SafeRoute: Pioneering Online Ride Booking with Pega & ML-Pow...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Rajalakshmi, D. Immaculate, P.S. Pandi, S. Senthil Pavitra, A. Rehash Rushmi Vijayalakshmi, V. Sharanya, S. Srm Institute of Science and Technology Data Science and Business Systems Chennai India Sri Sai Ram Institute of Technology Chennai Master of Business Administration India Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

The advent of online ride booking services has revolutionized the transportation industry, providing users with convenient and efficient means of commuting. However, ensuring the safety of passengers and drivers remains a paramount concern. This project introduces an innovative solution that leverages Pega, a leading business process management platform, coupled with machine learning algorithms to enhance the safety measures within an online ride booking service. The proposed system integrates seamlessly into the existing online ride booking infrastructure and introduces advanced safety mechanisms based on realtime data analysis. Machine learning models are employed to predict and mitigate potential risks, ensuring a secure and reliable experience for both passengers and drivers. © 2024 IEEE.

关键词： Reservation systems

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Budiarto, Arif Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

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Perceptual-Aware and Restorable Real-Time Image Downscaling

Perceptual-Aware and Restorable Real-Time Image Downscaling

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Niu, Yuzhen Zheng, Luwei Lin, Jiaqi Wu, Jianbin Liu, Wenxi College of Computer and Data Science Fuzhou University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Image downscaling has been a classical problem and has recently been linked to super-resolution (SR). In this paper, we aim to propose a learning-based image downscaling model, FastDownscaler, which can efficiently produce low-resolution (LR) images that not only preserve the rich details of the original high-resolution images but also be highly restorable for existing SR models. We first present two separate lightweight networks with different upsampling losses, the bilinear loss and the bicubic loss, which are better for SR restoration and LR downscaling, respectively. To produce versatile LR images, we then propose to distill bilinear loss guided network with bicubic loss guided one. To our best knowledge, we establish the first image downscaling quality assessment dataset to evaluate the downscaling performance. Experimental results demonstrate the superior performance of the proposed model on image downscaling and SR. Furthermore, our model can achieve over 600 FPS for downscaling a 1920\times 1280 image. © 2022 IEEE.

关键词： Optical resolving power

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DTRadar: Accelerating Search Process of Decision Trees in Packet Classification 28

DTRadar: Accelerating Search Process of Decision Trees in Pa...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Yu, Jiashuo Huang, Long Zhu, Longlong Zhang, Dong Wu, Chunming College of Computer and Data Science Fuzhou University Fuzhou China Zhicheng College Fuzhou University Fuzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350300482

Packet classification is an essential part of computer networks. Existing algorithms propose a partition process to address the memory explosion problem of the decision tree algorithm caused by the huge number of rules with multiple fields. However, the search process requires traversing multiple trees generated by the partition, which reduces the search efficiency. The existing algorithms take simple approaches to optimize the search process, which is low efficiency or high hardware overhead. In this paper, we propose DTRadar, a framework for expediting the decision tree packet lookup process. Its key idea is building an abstract One-Big-Tree(OBT) for multiple decision trees by establishing the middle data structure. DTRadar considers each decision tree as a splittable tree and organizes these subtrees by intermediate data structures. Extensive experiments show that DTRadar benefits existing decision tree-based solutions in classification time by 61.60%, and the memory footprint only increased by 4.21% on average. © 2023 IEEE.

关键词： Decision trees

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