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arXiv 2024年

作者： Hazrat, Roozbeh Mukherjee, Promit Pask, David Sardar, Sujit Kumar Centre for Research in Mathematics and Data Science Western Sydney University SydneyNSW2150 Australia Department of Mathematics Jadavpur University Kolkata700032 India School of Mathematics and Applied Statistics University of Wollongong WollongongNSW2522 Australia

Given a row-finite higher-rank k-graph Λ, we define a commutative monoid TΛ which is a higher-rank analogue of the talented monoid of a directed graph introduced in (J. Algebra 384 (2013), 242-266) and (J. Algebra 547 (2020), 430-455). The talented monoid TΛ is canonically a k-monoid with respect to the action of state shift. This monoid coincides with the positive cone of the graded Grothendieck group K0gr(KPk(Λ)) of the Kumjian-Pask algebra KPk(Λ) with coefficients in a field k. The aim of the paper is to investigate this k-monoid as a capable invariant for classification of Kumjian-Pask algebras. We establish the k-monoid isomorphism TΛ ≅ TGΛ = Typ(GΛ ×c k), the type monoid of the inverse semigroup of the compact open bisections of the skew-product groupoid GΛ ×c k where c: GΛ → k is the canonical cocycle. From this it follows that TΛ is a refinement monoid. If k acts freely on TΛ (i.e., if TΛ has no nonzero periodic element), then we show that the k-graph Λ is aperiodic in the sense of Lewin and Sims (Math. Proc. Camb. Phil. Soc. 149 (2010), 333-350). The converse is also proved to be true provided Λ has no sources and TΛ is atomic. Moreover in this case, we provide a talented monoid characterization for strongly aperiodic k-graphs. We prove that for a row-finite k-graph Λ without sources, cofinality is equivalent to the simplicity of TΛ as a k-monoid. In view of this we provide a talented monoid criterion for the Kumjian-Pask algebra KPR(Λ) of Λ over a unital commutative ring R to be graded basic ideal simple. We also describe the minimal left ideals of KPk(Λ) in terms of the aperiodic atoms of TΛ and thus obtain a monoid theoretic characterization for Soc(KPk(Λ)) to be an essential ideal. These results help us to characterize semisimple Kumjian-Pask algebras through the lens of TΛ © 2024, CC BY.

关键词： Directed graphs

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Tobacco Plant Disease dataset 14

Tobacco Plant Disease Dataset

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： Computer vision

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Handwritten Digits Prediction System using Convolution Neural Network for MINIST dataset

Handwritten Digits Prediction System using Convolution Neura...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Shweta Chauhan Rahul Kumar Bibek Kumar Apex Institute of Technology (CSE University Centre for Research and Development Chandigarh University Mohali India ECE Deaprtment GLA University Mathura Uttar Pardesh India School of Data Science Symbiosis University of Applied Sciences Indore Madhya Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

A dramatic change has been occurred in machine learning with the development of Artificial Neural Network (ANN), making it more artificially intelligent in recent years. In addition to surveillance, health, medicine, sports, robotics, and drones, deep learning is used in many other fields as well due to its diverse range of applications. The Convolutional Neural Network (CNN), which blends Artificial Neural Networks and current deep learning strategies, is at the center of phenomenal advances in deep learning. Using varying numbers of hidden layers and epochs, we investigate the variation in accuracy of CNN to classify handwritten numbers and make comparisons between the different accuracy levels. A stochastic gradient descent algorithm and backpropagation has been used to train the network model. This experiment has been designed to evaluate the performance of CNN using the MNIST dataset.

关键词： Deep learning Accuracy Convolution Surveillance Stochastic processes Prediction algorithms Convolutional neural networks Robots Sports Drones

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data Visualization and Analysis with Machine Learning for the USA's COVID-19 Prediction 8

Data Visualization and Analysis with Machine Learning for th...

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8th International Conference on Fuzzy Systems and data Mining, FSDM 2022

作者： Yuen, Man-Ching Cheng, Wing-Fat Wong, Hiu-Hong IFREE Group Innovation and Research Centre Department of Applied Data Science Hong Kong Shue Yan University Hong Kong Hong Kong Department of Information Technology Vocational Training Council Hong Kong Hong Kong Department of Information Technology University of the West of England Bristol United Kingdom

ISBN: (纸本)9781643683461

Recently, many research works adopt machine learning to provide accurate predictions on the COVID-19 pandemic. In this paper, we design and develop a web system which adopts machine learning methodologies to provide data analysis and data visualization. For experiment analytics results in the system, we find that SVM method outperforms LR method in every use case. We propose a web-based user-friendly and intuitive COVID-19 information hub, which can improve data accessibility to the public and allow more accurate decision-making to help fight the pandemic. © 2022 The authors and IOS Press.

关键词： COVID-19

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Synergetic effects of multiple junction and surface hydroxyl in Cu/CuO/Cu2O/TiO2 heterostructures towards highly efficient photocatalysts for hydrogen generation

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Materials science for Energy Technologies 2025年 8卷 131-142页

作者： Subagyo, Riki Anindika, Garcelina Rizky Aufkani, Afif Akmal Zhang, Lei Ardhyananta, Hosta Burhan, R.Y. Perry Nugraheni, Zjahra Vianita Akhlus, Syafsir Bahruji, Hasliza Prasetyoko, Didik Wellia, Diana Vanda Ivansyah, Atthar Luqman Arramel Kusumawati, Yuly Department of Chemistry Faculty of Science and Analytic Data Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Jl PUSPIPTEK South Tangerang Banten15314 Indonesia Department of Physics National University of Singapore Singapore117551 Singapore Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang25163 Indonesia Master Program in Computational Science Faculty of Mathematics and Natural Science Institut Teknologi Bandung Jalan Ganesha No. 10 Bandung40132 Indonesia Instrumentation and Computational Physics Research Group Department of Physics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jalan Ganesha No.10 West Java Bandung40132 Indonesia

The implementation of titanium dioxide (TiO2) as a photocatalyst material in hydrogen (H2) evolution reaction (HER) has embarked renewed interest in the past decade. Rapid electron-hole pairs recombination and wide band gap of a photo-sensitive material of TiO2 are detrimental toward the targeted catalytical reaction. In this study, we present the rational design, fabrication, photocatalytic performance of TiO2-Cu/CuO/Cu2O heterostructures (CuTi) using viable chemical reduction method. The Z-scheme and S-scheme are succesfully generated across the TiO2/CuO/Cu2O interfaces, while the Schottky junction arises on the Cu perimeters. This is evidenced from the blue shifted about 0.3 eV of Cu 2p core level determined by using X-ray photoemission spectroscopy (XPS), in combination with the formation of inverse V-shape of the Mott-Schottky plots. In addition, we find that Cu/CuO/Cu2O facilitates photon absorption range up to the visible region. The multiple heterojunction and the large number of OHsurface enhanced charge carrier transfer are associated to the suppression of photoluminescence (PL) intensity, high surface hydroxyl (OHsurface) density in CuTi probed by XPS, and fast electron transfer based on the electrochemical measurements. The presence of OHsurface inhibits the recombination of electron. A significant H2 photogeneration rate enhancement is achieved when an optimized 5 wt% Cu/CuO/Cu2O concentration is used on TiO2 to achieve 7,157.19 μmol·g−1 (1,789.30 μmol·g−1·h−1). Based on this finding, zero emission energy innitiative could be materialized under multiple heterojunctions in photocatalytic process is beneficial for enhancing the H2 production. © 2025 The Authors

关键词： Titanium dioxide

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Automatic near real-time quality control tests for biofouling effect on measurement data

Automatic near real-time quality control tests for biofoulin...

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OCEANS

作者： Astrid Marie Skålvik Anders Tengberg Kjell-Eivind Frøysa Ranveig N. Bjørk Camilla Saetre Department of Physics and Technology University of Bergen Bergen Norway Aanderaa Data Instruments AS Bergen Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences/NORCE Norwegian Research Centre Bergen Norway NORCE Norwegian Research Centre Bergen Norway

The reliability of water quality measurement is crucial for sustainable use of ocean resources, climate and ecosystem models, and industrial applications. However, measurement stations in remote locations face limitations in terms of power, communication, and maintenance, posing challenges for data quality. Even though some basic (near) real-time automatic tests are proposed in oceanographic measurement guidelines, time- and resource consuming Delayed Mode Quality Control is still required before using measurement data in forecasting, models, or decision-making. To design effective quality control tests for more autonomous sensors with self-validating capabilities in real-time, a good understanding of expected environmental effects or errors on sensor signals is *** paper focuses on the effect of biofouling on the measurement of selected water quality parameters such as conductivity, oxygen, and turbidity. Biofouling remains a major issue despite research on biofouling protection and anti-biofouling sensor design. Biofouling growth on underwater sensors can increase measurement errors and uncertainty, result in shorter operation times, and require costly manual work related to retrieval, cleaning, and re-deployment. For some measurement technologies, biofouling can result in noise, while for others, it may cause systematic drift or delay signal ***, we propose quality control tests designed to automatically detect and assess the impact of biofouling on sensor signals. These tests are applied to measurement data sets with a known presence of biofouling from Austevoll (Norway). We comment on the challenges of designing tests and setting adequate *** show that a detailed understanding of biofouling effect on sensors is crucial for designing effective near real-time quality control procedures. Automatic, in-situ tests can save costs related to manual data quality control and increase data quality, thereby enabling well-informed decisions i

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Actively Targeting Redox-Responsive Multifunctional Micelles for Synergistic Chemotherapy of Cancer

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ACS Omega 2024年第32期9卷 34268-34280页

作者： Darge, Haile Fentahun Addisu, Kefyalew Dagnew Tsai, Hsieh-Chih Birhan, Yihenew Simegniew Hanurry, Endris Yibru Mekonnen, Tefera Worku Gebrie, Hailemichael Tegenu Arunagiri, Vinothini Thankachan, Darieo Wu, Tsung-Yun Lai, Juin-Yih Chang, Hao-Ming Huang, Chun-Chiang Wu, Szu-Yuan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology College of Medicine and Health Science Bahir Dar University Centre for Ocular Research & Education (CORE) School of Optometry and Vision Science University of Waterloo Institute of Technology Bahir Dar University Advanced Membrane Materials Center National Taiwan University of Science and Technology R&D Center for Membrane Technology Chung Yuan University Department of Chemistry College of Natural and Computational Sciences Debre Markos University School of Medicine Health Science College Addis Ababa University Division of General Surgery Tri-Service General Hospital National Defense Medical Center Taiwan Instrument Research Institute National Applied Research Laboratories Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Division of Radiation Oncology Department of Medicine Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Department of Healthcare Administration College of Medical and Health Science Asia University Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Graduate Institute of Business Administration Fu Jen Catholic University Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University

Stimuli-responsive polymeric micelles decorated with cancer biomarkers represent an optimal choice for drug delivery applications due to their ability to enhance therapeutic efficacy while mitigating adverse side effects. Accordingly, we synthesized a digoxin-modified novel multifunctional redox-responsive disulfide-linked poly(ethylene glycol-b-poly(lactic-co-glycolic acid) copolymer (Bi(Dig-PEG-PLGA)-S₂) for the targeted and controlled release of doxorubicin (DOX) in cancer cells. Within the micellar aggregate, the disulfide bond confers redox responsiveness, while the presence of the digoxin moiety acts as a targeting agent and chemosensitizer for DOX. Upon self-assembly in aqueous solution, Bi(Dig-PEG-PLGA)-S₂ formed uniformly distributed spherical micelles with a hydrodynamic diameter (D_h) of 58.36 ± 0.78 nm and a zeta potential of −24.71 ± 1.01 mV. The micelles exhibited desirable serum and colloidal stability with a substantial drug loading capacity (DLC) of 6.26% and an encapsulation efficiency (EE) of 83.23%. In addition, the release of DOX demonstrated the redox-responsive behavior of the micelles, with approximately 89.41 ± 6.09 and 79.64 ± 6.68% of DOX diffusing from DOX@Bi(Dig-PEG-PLGA)-S₂ in the presence of 10 mM GSH and 0.1 mM H₂O₂, respectively, over 96 h. Therefore, in HeLa cell lines, DOX@Bi(Dig-PEG-PLGA)-S₂ showed enhanced intracellular accumulation and subsequent apoptotic effects, attributed to the targeting ability and chemosensitization potential of digoxin. Hence, these findings underscore the promising characteristics of Bi(Dig-PEG-PLGA)-S₂ as a multifunctional drug delivery vehicle for cancer treatment. © 2024 The Authors. Published by American Chemical Society

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Real-time Hand Gesture Recognition Using Python and Web Application

Real-time Hand Gesture Recognition Using Python and Web Appl...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Meenu Patel Saksham Rao Shweta Chauhan Bibek Kumar Apex Institute of Technology CSE Chandigarh University Mohali Punjab India Apex Institute of Technology CSE University Centre for Research & Development Chandigarh University Mohali Punjab India School of Data Science Symbiosis University of Applied Sciences Indore Madhya Pradesh India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The recognition and tracking of hand gestures are essential elements in human-computer interaction systems, providing intuitive control and facilitating interaction with a wide range of devices and applications. We have developed a hand gesture detection system using Python and JavaScript. Our system uses MediaPipe, an open-source framework for machine learning-based perception tasks, to accurately identify and classify hand gestures. MediaPipe handles hand tracking, interpreting the hand curl and movement of hand gestures with high precision. We've built the frontend interface using HTML, CSS, and JavaScript. By combining these technologies, we've created a user-friendly interface where you can interact with the system through hand gestures captured via a webcam. Additionally, we've added our custom machine-learning methods to detect and recognize various hand *** system has numerous potential applications, including hand-sign translation and gesture-based control for smart devices. By offering an efficient and accessible solution, our project advances human-computer interaction paradigms, enabling more natural and intuitive interactions between users and machines.

关键词： Hands Human computer interaction Computer vision Technological innovation Translation Webcams Tracking Machine learning Gesture recognition Real-time systems

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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