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Detection of features from the internet of things customer attitudes in the hotel industry using a deep neural network model

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Measurement: Sensors 2022年 22卷

作者： Rajesh, Sudha Abd Algani, Yousef Methkal Al Ansari, Mohammed Saleh Balachander, Bhuvaneswari Raj, Roop Muda, Iskandar Kiran Bala, B. Balaji, S. Dept. of Computational Intelligence College of Engineering and Technology School of Computing SRMIST Kattankulathur Chennai India Department of Mathematics Sakhnin College Israel College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Lecturer in Economics Education Department Government of Haryana Haryana Panchkula India Department of Doctoral Program Faculty Economic and Business Universitas Sumatera Utara 20222 Jl. Prof TM Hanafiah 12 USU Campus Padang bulan Medan20155 Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Department of CSE Panimalar Engineering College Chennai India Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel

Tourism and the hotel business have benefited greatly from the use of digital social networking. Using social big data research, the application of deep learning seems to have been beneficial in a marketing strategies and customer preference estimate. Recognizing human psychology, which is critical to industrial success, has benefited greatly from digital technology and social media. The Internet of Things (IoT) provides a chance for a hotel sector to improve the customer experience although lowering operational expenses. The ratings are determined by the following factors: Value, Apartment, Location, Hygiene, Front Office, Facilities, Professional Service, Internet, and Parking. Traditional techniques which anticipate hotel evaluations through minimal precision add difficulty to the rating assessment. As a result, efficient deep learning algorithms are employed to evaluate reviews designed to help consumers in selecting better hotels. To predict qualities, multiple classification techniques, including convolutional neural network-based deep learning (CNN-DL) and support vector machine (SVM) network-based deep learning, were used in this research. The system examines system efficiency by using the TripAdvisor website, this is a well American database. The research results reveal that the CNN-DL method outperforms another method in terms of classification efficiency and failure *** graphical results could also be utilized to enhance the effectiveness of the suggested model and offer insights into response tactics, demonstrating the study's academic and conceptual achievements. Although it is feasible to conclude from such research that the possibility of IoT within the hotel industry has not yet been fully investigated, as researchers commonly speculate on using IoT for implementations that might quickly be of involvement to a hotel sector, but refuse to recognize that possibility as a massive market. © 2022 The Author(s)

关键词： Hotels

Low-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems

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IFAC-PapersOnLine 2021年第7期54卷 463-468页

作者： Konstantin Usevich Philippe Dreesen Mariya Ishteva Université de Lorraine CNRS CRAN Nancy France KU Leuven Dept. Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven Department of Computer Science ADVISE-NUMA campus Geel Belgium

We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the case of parallel Wiener-Hammerstein systems, such methods require nontrivial constraints on the factors of the decompositions. In this paper, we propose an entirely different approach: by using special sampling (operating) points for the Jacobian of the nonlinear map from past inputs to the output, we can show that the Jacobian matrix becomes a linear projection of a tensor whose rank is equal to the number of branches. This representation allows us to solve the identification problem as a tensor recovery problem.

关键词： Block structured system identification parallel Wiener-Hammerstein systems Volterra kernels low-rank tensor recovery canonical polyadic decomposition

A showcase of the use of autoencoders in feature learning applications?

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arXiv 2020年

作者： Charte, David Charte, Francisco del Jesus, María J. Herrera, Francisco Andalusian Research Institute in Data Science and Computational Intelligence Dept. of Computer Science and Artificial Intelligence Universidad de Granada Periodista Daniel Saucedo Aranda s/n Granada18071 Spain Andalusian Research Institute in Data Science and Computational Intelligence Computer Science Dept. Universidad de Jaén Campus Las Lagunillas s/n Jaén23071 Spain

Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing. This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, ruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives. This manuscript was accepted as conference paper in IWINAC 2019. The final authenticated publication is available online at https://***/10. 1007/978-3-030-19651-6 40. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

Few-shot Class-incremental Learning for 3D Point Cloud Objects

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arXiv 2022年

作者： Chowdhury, Townim Cheraghian, Ali Ramasinghe, Sameera Ahmadi, Sahar Saberi, Morteza Rahman, Shafin Dept. of Electrical and Computer Engineering North South University Bangladesh School of Engineering Australian National University Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia Australian Institute for Machine Learning University of Adelaide Australia Business School The University of New South Wales Australia School of Computer Science and DSI University of Technology Sydney Australia

Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain. Code is available at: https://***/townim-faisal/FSCIL-3D. © 2022, CC BY.

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Crystal Structure Prediction by Joint Equivariant Diffusion

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arXiv 2023年

作者： Jiao, Rui Huang, Wenbing Lin, Peijia Han, Jiaqi Chen, Pin Lu, Yutong Liu, Yang Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Stanford University United States

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures—the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation. Code is available at https://***/jiaor17/DiffCSP. © 2023, CC BY-SA.

关键词： Geometry

DeepMerge: Deep-learning-based Region-Merging for Image Segmentation

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arXiv 2023年

作者： Lv, Xianwei Persello, Claudio Li, Wangbin Huang, Xiao Ming, Dongping Stein, Alfred School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China Hebei Key Laboratory of Marine Perception Network and Data Processing Northeastern University Qinhuangdao066004 China Dept. of Earth Observation Science Faculty ITC University of Twente Enschede7500AE Netherlands State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China The Department of Geosciences University of Arkansas FayettevilleAR United States Beijing100083 China

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods. © 2023, CC BY.

关键词： Image segmentation

The lack of return to isotropy in decaying, axisymmetric, saffman turbulence 14

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The lack of return to isotropy in decaying, axisymmetric, sa...

14th European Turbulence Conference, ETC 2013

作者： Davidson, P.A. Okamoto, N. Kaneda, Y. Dept. of Engineering University of Cambridge Trumpington Street CambridgeCB2 1PZ United Kingdom Centre. Computational Science Nagoya University Nagoya464-8603 Japan Aichi Institute of Technology 470-0392 Japan

Dynamic placement of rapidly deployable mobile sensor robots using machine learning and expected value of information

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arXiv 2021年

作者： Agogino, Alice Jang, Hae Young Rao, Vivek Batra, Ritik Liao, Felicity Sood, Rohan Fang, Irving Hu, R. Lily Shoichet-Bartus, Emerson Matranga, John Dept. of Mechanical Engineering University of California BerkeleyCA94720 United States Dept. of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Division of Computing Data Science and Society Dept. of Mathematics University of California BerkeleyCA94720 United States Google Research Google Mountain ViewCA94043 United States Division of Computing Data Science and Society University of California BerkeleyCA94720 United States Business Incubation and Acceleration AVEVA San LeandroCA94577 United States

Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios. © 2021, CC BY-NC-ND.

关键词： Decision trees

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

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arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting