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arXiv 2022年

作者： Alzubaidi, Fatimah Mostaghimi, Peyman Niu, Yufu Armstrong, Ryan T. Mohammadi, Gelareh McClure, James E. berg, Steffen School of Minerals and Energy Resources Engineering University of New South Wales Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia National Security Institute Virginia Tech BlacksburgVA24061 United States Shell Global Solutions International B.V. Grasweg 31 Amsterdam1031HW Netherlands

based on the phenomenological extension of Darcy's law, two-fluid flow is dependent on a relative permeability function of saturation only that is process/path dependent with an underlying dependency on pore structure and wettability. For applications, fuel cells to underground CO2 storage, it is imperative to determine the effective phase permeability relationships where the traditional approach is based on the inverse modelling of time-consuming experiments. The underlying reason is that the fundamental upscaling step from pore to Darcy scale, which links the pore structure of the porous medium to the continuum hydraulic conductivities, is not solved. Herein, we develop an Artificial Neural Network (ANN) that relies on fundamental geometrical relationships to determine the mechanical energy dissipation during creeping immiscible two-fluid flow. The developed ANN is based on a prescribed set of state variables based on physical insights that predicts the effective permeability of 4,500 unseen pore-scale geometrical states with R2 = *** Codes 76S05 (Primary), 76T06 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Pore structure

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SD-measure: a social distancing detector

arXiv

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arXiv 2020年

作者： Gupta, Savyasachi Kapil, Rudraksh Kanahasabai, Goutham Joshi, Shreyas Srinivas Joshi, Aniruddha Srinivas B.Tech Department of Computer Science and Engineering National Institute of Technology Warangal Telangana506004 India

The practice of social distancing is imperative to curbing the spread of contagious diseases and has been globally adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages. The proposed framework leverages the Mask R-CNN deep neural network to detect people in a video frame. To consistently identify whether social distancing is practiced during the interaction between people, a centroid tracking algorithm is utilised to track the subjects over the course of the footage. With the aid of authentic algorithms for approximating the distance of people from the camera and between themselves, we determine whether the social distancing guidelines are being adhered to. The framework attained a high accuracy value in conjunction with a low false alarm rate when tested on Custom Video Footage Dataset (CVFD) and Custom Personal Images Dataset (CPID), where it manifested its effectiveness in determining whether social distancing guidelines were practiced. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Prevention from Gas Devastation

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Journal of Physics: Conference Series 2021年第1期1797卷

作者： Sanghita Datta Ankita Sah Sidharth Gautam Upamita Das Dr. Saikat Maity B. Tech Computer Science Engineering JIS University

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity but also for the surrounding areas. The idea behind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected by the system, the concerned people around will receive messages as well as the alarm rings.

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On multi-objective minimum spanning tree under the framework of uncertainty theory

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AIP Conference Proceedings 2023年第1期2851卷

作者： Swapna Halder Saibal Majumder Arindam biswas bijoy Kumar Mandal Sheng-Lung Peng Department of Computer Science and Engineering St. Thomas College of Engineering & Technology Kolkata West Bengal India Centre for IOT and AI Integration with Education -Industry-Agriculture Kazi Nazrul University Asansol West Bengal India Department of Computer Science and Engineering (Data Science) Dr. B. C. Roy Engineering College Durgapur West Bengal India School of Mines and Metallurgy Kazi Nazrul University Asansol West Bengal India Department of Computer Science and Engineering NSHM Knowledge Campus Durgapur West Bengal India Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan Taiwan

Aminimum spanning tree with multiple objectives is one type of NP hard problem, and it has a wide application area in ourdaily life. Modelling real world problems, requires many associated parameters which are not well-defined and are indeterminate in nature. Thus, it is rational to consider these indeterminate parameters while modelling real life problems. This article, investigates onthe study of minimum spanning tree with multiple objectives which are uncertain in nature. The proposed multiple objectives minimum spanning tree has two uncertain parameters associated with it, they are time and cost. both of these parameters are indeterminate in nature and characterizedas uncertain variables. The expected value model (EVM) of multiple objectives minimum spanning tree is formulatedin this study and convex combination method is used to solve this problem. Finally to achieve the nondominated solutions of this problem, we have solved the EVM by varying the related weights of its objectives.

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High resolution spatial modelling of soil parent material: An initial investigation study using random forest machine learning techniques

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Journal of South American Earth sciences 2025年 162卷

作者： Vijayakumar, A. Alshardan, Amal Alghamdi, Abdulaziz G. Durai, S. Department of Civil Engineering V.S.B. Engineering College Tamilnadu Karur 639111 India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Soil Sciences College of Food and Agricultural Sciences King Saud University P.O. Box 2460 Riyadh 11451 Saudi Arabia Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai 600062 India

This study uses Random Forest machine learning to explore the distribution of soil parent material in Morelos, Mexico. Elemental composition was determined using a portable X-ray fluorescence (pXRF) spectrometer, and soil variability was examined using magnetic susceptibility (MS) measurements over the A and b horizons. The goal was to improve soil categorisation and mapping accuracy to promote sustainable land management and agricultural operations. The statistical study indicated substantial differences between soil horizons: Magnesium content ranged from 7.1 % to 15.7 % (mean: 11.4 %) in the A horizon and from 8.0 % to 20.9 % (mean: 10.4 %) in the b horizon;iron concentrations exhibited lower variability, with mean values of 8.9 % (A horizon) and 7.4 % (b horizon);calcium displayed the most significant variation, with a maximum of 28.5 % in the b horizon;silicon remained relatively stable, with mean values of 3.8 % (A horizon) and 4.0 % (b horizon);and magnetic susceptibility showed substantial dif The Random Forest model accurately reflected geographic variations in soil parent material, emphasizing important geochemical and mineralogical distinctions. The findings highlight machine learning's potential for high-resolution soil mapping and categorisation. This approach lays the groundwork for enhanced soil fertility assessments, erosion management, and land-use planning. Future study should include additional geographic and environmental factors to improve model accuracy and prediction capabilities. © 2025 Elsevier Ltd

关键词： Geochemical analysis Machine learning Magnetic susceptibility pXRF spectrometry Random forest Soil classification Soil parent material Spatial modelling

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A study on hybrid recommender system with deep learning and deployment in big data

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Test engineering and Management 2019年第11-12期81卷 1869-1875页

作者： Ramanujam, Srivats S. Sivaneshwar, P. Naren, J. Madhumitha, S. Vithya, G. B. Tech Computer Science and Engineering School of Computing SASTRA Deemed University Thanjavur India B. Tech Information and Communication Technology School of Computing SASTRA Deemed University Thanjavur India School of Computing SASTRA Deemed University Tirumalaisamudram ThanjavurTamil Nadu India B. Tech Electronics and Communication Engineering School of Computing SASTRA Deemed University Thanjavur India School of Computing KL University Vijayawada India

A recommender system is a filtering tool that provides customized suggestions of products to users by using various techniques. A hybrid recommender system combines the approaches of two or more techniques, primarily focusing on Content-based recommender systems and Collaborative recommender systems, thus proving better suggestions by exploiting the advantages of both the strategies. This paper provides a detailed review of the various ways to implement a Hybrid Recommender System, enabling the reader to get a bird’s eye view on the concept. The study allows the user to understand the relationship between data characteristics and the relative performance of various approaches to a Hybrid Recommender System. The flow of the research starts with a description of the initial models of Recommender Systems, followed by a study on the application of Machine Learning and Deep Learning algorithms to the Hybrid Recommenders. The contributions to the study are dichotomous: 1) to identify and discuss the various techniques for Hybrid Recommendation Systems via a Systematic literature review and 2) to compare the various techniques to get a clear view of the efficiency and the accuracy of each technique. At the end of the study, the reader understands the development flow of Hybrid Recommender Systems and gets a clear idea of how various Recommender Systems work. It is observed that the technique which incorporates Deep Learning provides better results. © 2019 JPR Solutions. All rights reserved.

关键词： Recommender systems

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 603 LNNS卷 ix-x页

作者： Julián, Vicente Carneiro, João Alonso, Ricardo S. Chamoso, Pablo Novais, Paulo Department of Computer Systems and Computation Institut Valencià d’Intel·ligència Artificial Universitat Politècnica de València Valencia Spain ISEP/IPP—Engineering Institute Polytechnic of Porto Porto Portugal Deep Tech Lab IoT Digital Innovation Hub AIR Institute Valladolid Spain Research Group IoT Digital Innovation Hub Department of Computer Science Faculty of Science University of Salamanca Salamanca Spain Departamento de Informática ALGORITMI Centre University of Minho Braga Portugal

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Synthesis and characterization of tritendate Schiff base rare earth nano metal complexes

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Materials Today: Proceedings 2021年 34卷 453-459页

作者： A. Kavitha D. Easwaramoorthy T. Thangeeswari G. Parthipan S. Shanmugan Thamean Ansari Department of Chemistry Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu 600 062 India Department of Chemistry B.S. Abdur Rahman University Vandalur Chennai 600 048 Tamil Nadu India Department of Physics Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu 600 062 India Department of Computer Science Engineering Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu 600 062 India

The tridentate Schiff base ligand (1-pyridin-3-ylimino-ethyl benzene-1,3-diol were synthesized using precursor source 3-aminopyridine with 2,4-dihydroxyacetophenone in methanol at 60 °C. The prepared Schiff base ligand doped with rare earth metal complex such as Ce(III), Pr(III) and Er (III) ion, respectively and it is denoted as [Ln(L)(NO 3 ) 2 (H 2 O)]·NO 3 . The Schiff base ligand and nano metal complexes have been characterized by UV–Visible, FTIR, 1 H NMR, fluorescence spectrum (FL). Also the thermal properties of synthesized rare earth metal complexes were analysed by using TGA & DTA thermal analysis. These characterisation results given exact structural elucidation and optical properties of synthesized rare earth metal ions doped Schiff base nano metal complex. In UV spectra of the cerium, praseodymium and erbium nitrate complexes, there is no noticeable absorption in the visible region (380–700 nm) due to the shielding effect of rare earth 4f orbital electrons. FTIR spectra prove that the various functional groups present in doped Schiff base metal complexes. The blue shift in the, fluorescence spectra of lanthanide (III) complex indicates that the doped lanthanum ions have an apparent influence on the energy absorption of the complex system through well co-ordination with ligand. The purity and molecular formulae of synthesized Schiff base ligands and Ce(III), Pr(III) and Er (III) metal complexes were identified by H 1 NMR, C 13 NMR spectroscopy. Thermal analysis (TGA & DTA) concludes that synthesized rare earth metal complexes with Schiff base ligand have adequate thermal stability.

关键词： Schiff Nano Metal complexes Antimicrobial activity benzene-1 3-diol

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Modeling and prediction of nonlinear cable slab dynamics using Koopman operators

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Mechatronics 2025年 110卷

作者： Michael Pumphrey Almuatazbellah M. boker Mohammad Al Saaideh Natheer Alatawneh Yazan M. Al-Rawashdeh Khaled Aljanaideh Mohammad Al Janaideh School of Engineering University of Guelph Guelph ON N1G 2W1 Canada Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA Department of Electrical and Computer Engineering Memorial University St. John’s NL A1B 3X5 Canada School of Computer Science and Engineering Sacred Heart University 3135 Easton Turnpike Fairfield CT USA Department of Electrical Engineering Al-Zaytoonah University of Jordan Amman Jordan Department of Aeronautical Engineering Jordan University of Science and Technology Irbid 22110 Jordan University of Jordan Amman Jordan Fraunhofer Institute for Applied Optics and Precision Engineering Jena Germany

A novel approach for modeling the nonlinear dynamics of cable slabs using Koopman operator theory is presented. Cable slab dynamics are a critical challenge in precision motion systems, as the cables can induce undesired vibrations and disturbances on motion stages. To address this, a higher-dimensional state-space model with nonlinear observable functions is developed to approximate the cable slab dynamics. The proposed model achieves a prediction error within ∼ 1 % over the specified motion range and demonstrates robustness in predicting untrained, randomized, acyclic cable slab motions. A systematic evaluation of various observable functions was conducted to minimize the modeling errors, leading to an optimized model with fractional-order exponents. When compared with a neural network-based state-space model (NN-SS), the Koopman approach demonstrated faster training and better performance. For force prediction, the Koopman approach achieved a reduction of three-quarters in maximum error when compared with the NN-SS method. This work offers a concise and experimentally validated analytical framework specifically for developing accurate predictive models of nonlinear cable slab dynamics.

关键词： Koopman operator Cable slab Motion control Nonlinear dynamics Data-driven modeling System identification

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Mining social network data for predictive personality modelling by employing machine learning techniques

Mining social network data for predictive personality modell...

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International Conference on Computational Advancement in Communication Circuit and System, ICCACCS 2018

作者： Sengupta, Arjun Ghosh, Anupam Tata Consultancy Services Ltd Candor Kolkata One Hi-Tech Structures Pvt. Ltd IT/ITES SEZ Block G III 5th Floor Tower B-II New Town Kolkata700160 India Department of Computer Science & Engineering Netaji Subhash Engineering College Techno City Garia Kolkata700152 India

ISBN: (纸本)9789811386862

Facebook, Twitter, LinkedIn and Tumblr are online social networking platforms where the users send and receive messages on the topic of their choice and express their sentiments. The usage of these sites has exponentially increased over the last few years, thereby increasing the information posted on online social media sites. The quantity of information/tweets keeps increasing on a daily basis. Twitter has become a stable platform to identify personality-related indicators and encrypted in user profiles and pages related to a subject. In this proposed work, we present a scalable real-time system for sentiment analysis of Twitter data. This work will collect tweets of the users in real time and thus provide a basis to identify each tweet into either positive or negative based on the mind-set of the user, thereby providing a real-time analysis of the users regarding a certain topic. The system relies on feature extraction from the tweets generated in real time. A supervised learning approach based on ensemble learning is used to train various classifiers based on the features extracted. A design and implementation in Flask and Celery has been carried out which contains the feature extraction and classification tasks. The system is scalable with respect to the size of the input data and the rate of data arrival. The merits of the proposed system in terms of scalability, performance and classification accuracy was evaluated experimentally. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Real time systems

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