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SSRN 2024年

作者： Pi, Yalong Wooley, Jack Mendes, Egleu D.M. Kaniyamattam, Karun Tedeschi, Luis O. Tao, Jian Duffield, Nick Institute of Data Science Texas A&M University College StationTX United States Department of Animal Science Texas A&M University College StationTX United States School of Performance Visualization and Fine Arts Texas A&M University College StationTX United States Department of Electrical and Computer Engineering Texas A&M University College StationTX United States

Livestock farming is under increasing pressure to improve efficiency and sustainability. Individual feed data are critical to feed cost management and animal disease and nutrition research. This paper proposes a framework (relying only on camera input) that integrates computer vision and unsupervised statistical clustering for individual livestock identification and feed grading. The framework leverages deep learning-based computer vision algorithms, including convolutional neural networks (CNN), to extract meaningful features from digital images captured in livestock farming environments. The proposed framework consists of three main components: animal presence sensing, animal identification (individual ID), and feed grading. Animal presence sensing is achieved through supervised transfer learning, where deep CNNs are trained to classify frames as either containing animals or not with labeled data. Animal identification employs unsupervised clustering algorithms such as k-means and DBSCAN to assign unique IDs to individual animals. This method automatically assigns different IDs to the incoming and outgoing animals without manual labeling. This ID task achieved an Adjusted Rand Index (ARI) up to 0.96 in our experiment, indicating a robust animal ID capability. A feed grading task for 6 classes was performed using pre-trained ResNet models with transfer learning on a manually labeled dataset, which accomplished an overall 91.16% accuracy when tested on unseen new data. This feed grading function can replace the traditional laborious manual examination. The proposed framework holds promise for enhancing the efficiency and sustainability of livestock farming practices by providing cheap and fast individual animal feeding data over long periods. © 2024, The Authors. All rights reserved.

关键词： Computer vision

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MultiNet with Transformers: A Model for Cancer Diagnosis Using Images

arXiv

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arXiv 2023年

作者： Barzekar, Hosein Patel, Yash Tong, Ling Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Health Informatics and Administration University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancer is a leading cause of death in many countries. An early diagnosis of cancer based on biomedical imaging ensures effective treatment and a better prognosis. However, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial cost. The cost makes large-scale biological image classification impractical. In this study, we provide unique deep neural network designs for multiclass classification of medical images, in particular cancer images. We incorporated transformers into a multiclass framework to take advantage of data-gathering capability and perform more accurate classifications. We evaluated models on publicly accessible datasets using various measures to ensure the reliability of the models. Extensive assessment metrics suggest this method can be used for a multitude of classification tasks. © 2023, CC BY.

关键词： Computer aided diagnosis

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Automatic tracing of mandibular canal pathways using deep learning

arXiv

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arXiv 2021年

作者： Dhar, Mrinal Kanti Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI United States

There is an increasing demand in medical industries to have automated systems for detection and localization which are manually inefficient otherwise. In dentistry, it bears great interest to trace the pathway of mandibular canals accurately. Proper localization of the position of the mandibular canals, which surrounds the inferior alveolar nerve (IAN), reduces the risk of damaging it during dental implantology. Manual detection of canal paths is not an efficient way in terms of time and labor. Here, we propose a deep learning-based framework to detect mandibular canals from CBCT data. It is a 3-stage process fully automatic end-to-end. Ground truths are generated in the preprocessing stage. Instead of using commonly used fixed diameter tubular-shaped ground truth, we generate centerlines of the mandibular canals and used them as ground truths in the training process. A 3D U-Net architecture is used for model training. An efficient post-processing stage is developed to rectify the initial prediction. The precision, recall, F1-score, and IoU are measured to analyze the voxel-level segmentation performance. However, to analyze the distance-based measurements, mean curve distance (MCD) both from ground truth to prediction and prediction to ground truth is calculated. Extensive experiments are conducted to demonstrate the effectiveness of the model. © 2021, CC BY-NC-SA.

关键词： Forecasting

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Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

引用

Computing and data Analytics (ICCDA), International Conference on

作者： Suresh Palarimath Pyingkodi Maran Wilfred Blessing N. R S. J Kavitha Cibi. A Subitha G. Sutherlin College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai India Data & Analytics / Visualization - Delivery Virtusa Consulting Services Private Limited Chennai India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector.

关键词： Measurement Instruments Zero shot learning Malware Mobile security Hazards Reliability Protection Smart phones Research and development

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Personal Protection Equipment Training as a Virtual Reality Game in Immersive Environments: Development Study and Pilot Randomized Controlled Trial

引用

JMIR Serious Games 2025年 13卷 e69021页

作者： Zhou, Liang Liu, Haoyang Fan, Mengjie Liu, Jiahao Yu, Xingyao Zhao, Xintian Zhang, Shaoxing Institute of Medical Technology Peking University Health Science Center Beijing China National Institute of Health Data Science Peking University Beijing China Beijing Forestry University Beijing China Visualization Research Center (VISUS) University of Stuttgart Stuttgart Germany Department of Healthcare-Associated Infection Management Peking University Third Hospital Beijing China Department of Otolaryngological Peking University Third Hospital Beijing China

Background: Proper donning and doffing of personal protection equipment (PPE) and hand hygiene in the correct spatial context of a health facility is important for the prevention and control of nosocomial infections. On-site training is difficult due to the potential infectious risks and shortages of PPE, whereas video-based training lacks immersion which is vital for the familiarization of the environment. Virtual reality (VR) training can support the repeated practice of PPE donning and doffing in an immersive environment that simulates a realistic configuration of a health facility. Objective: This study aims to develop and evaluate a VR simulation focusing on the correct event order of PPE donning and doffing, that is, the item and hand hygiene order in the donning and doffing process but not the detailed steps of how to don and doff an item, in an immersive environment that replicates the spatial zoning of a hospital. The VR method should be generic and support customizable sequencing of PPE donning and doffing. Methods: An immersive VR PPE training tool was developed by computer scientists and medical experts. The effectiveness of the immersive VR method versus video-based learning was tested in a pilot study as a randomized controlled trial (N=32: VR group, n=16;video-based training, n=16) using questionnaires on spatial-aware event order memorization questions, usability, and task workload. Trajectories of participants in the immersive environment were also recorded for behavior analysis and potential improvements of the real environment of the health facility. Results: Comparable sequence memorization scores (VR mean 79.38, SD 12.90 vs video mean 74.38, SD 17.88;P=.37) as well as National Aeronautics and Space Administration Task Load Index scores (VR mean 42.9, SD 13.01 vs video mean 51.50, SD 20.44;P=.16) were observed. The VR group had an above-average usability in the System Usability Scale (mean 74.78>70.0) and was significantly better than the video g

关键词： human computer interaction nosocomial infections control personal protection equipment PPE virtual reality training visualization

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Rotation-Inversion Isomerization of Tertiary Carbamates: Potential Energy Surface Analysis of Multi-Paths Isomerization Using Boltzmann Statistics

引用

CHEMPHYSCHEM 2023年第1期24卷 e202200442-e202200442页

作者： Jameson, Brian Glaser, Rainer Department of Chemistry Missouri University of Science and Technology Rolla Missouri 65401 USA Contribution: Conceptualization (supporting) Data curation (lead) Formal analysis (equal) Visualization (lead) Writing - original draft (equal) Writing - review & editing (supporting) Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (lead) Project administration (lead) Supervision (lead) Writing - original draft (equal) Writing - review & editing (lead)

The front cover artwork is provided by Prof. Rainer Glaser's group at the Missouri University of science and Technology. The image shows one of four potential energy surfaces generated from our rotation-inversion study of tertiary carbamates and highlights two of the eight possible transition state pathways between two ensembles of E- and Z-minima. In the context of synthetic studies of fluorinated carbamates R1O-CO-N(R-2)CH2CF3, we unexpectedly observed two sets of C-13 NMR quartets for the CF3 group and we needed to understand their origin. Read the full text of the Research Article at 10.1002/cphc.2022005442.

关键词： carbamates multipaths equilibria negative hyperconjugation rotation-inversion second-order saddle points

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Dielectric and electromagnetic interference shielding properties of nano-silicon carbide/montmorillonite and graphene nanoplatelets filled polyvinylidene fluoride/poly (3,4-ethylenedioxythiophene)-block-poly(ethylene glycol) blend nanocomposites (vol 140, e53510, 2023)

引用

JOURNAL OF APPLIED POLYMER science 2023年第15期140卷 e53510-e53510页

作者： Thanasekaran, G. Ahamed, M. B. Deshmukh, K. Kadlec, J. Department of Physics B. S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai India Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Investigation (lead) Writing - original draft (lead) New Technologies - Research Center University of West Bohemia Plzeň Czech Republic Contribution: Conceptualization (lead) Formal analysis (equal) Investigation (equal) Project administration (equal) Resources (equal) Validation (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Investigation (equal) Methodology (equal) Resources (equal) Visualization (equal)

Herein, hybrid polymer nanocomposite films comprising polyvinylidene fluoride (PVDF) and poly(3,4-ethylenedioxythiophene)-block-poly(ethylene glycol) (PEDOT-block-PEG) as matrices and silicon carbide nanoparticles (SiC NPs), montmorillonite (MMT) nanoclay, and graphene nanoplatelets (GNPs) as nanofillers were fabricated by solution casting. The structural, morphological, and thermal characteristics of the nanocomposite films were assessed using Fourier transform infrared spectroscopy (FTIR), x-ray diffraction (XRD), scanning electron microscope (SEM), thermogravimetric analyzer (TGA), and differential scanning calorimetry (DSC). The SEM results show the homogeneous distribution of nanofillers within the polymer matrix. The dielectric behavior and electromagnetic interference shielding efficiency (EMI SE) of the nanocomposites were also analyzed. The maximum dielectric constant (ε) and dielectric loss (tan δ) obtained are 47.77 and 12.08, respectively, at lower frequency (50 Hz, 150°C). The maximum EMI SE was observed to be 16.8 dB in the Ku-band region (12–18 GHz) for the nanocomposites with 20 wt% SiC, 8 wt% MMT, and 2 wt% GNPs loading. The EMI shielding was found to be absorption dominant and originated from the formation of a conductive network between hybrid nanofillers within the PVDF/PEDOT-block-PEG blend thereby improving the EMI SE values. These results suggest that PVDF/PEDOT-block-PEG/SiC/MMT/GNPs nanocomposites can be used as an efficient EMI shielding material.

关键词： dielectric properties EMI shielding morpholog PEDOT-block-PEG PVDF

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Harmonic attentive multimodal neural network for movie recommendation 4

Harmonic attentive multimodal neural network for movie recom...

引用

2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Wang, J.X. Guo, Y. Qi, T.M. Tang, Q.F. Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data and Internet Audience Shanghai200072 China

A multimodal neural network based on harmonic self-attention was proposed for movie recommendation. This method can deal with the multi-source data and learn representations of users and items well. The multimodal neural network mainly consists of three sub-networks, ResNet, Bert, and LSTM. In addition, the harmonic self-attention mechanism can explore the user's preference from the perspective of time. Experimental results show that compared with other three latest recommendation methods, HSMNN shows better performance in terms of HR and NDCG. © Published under licence by IOP Publishing Ltd.

关键词： Long short-term memory

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A multimodal deep learning architecture for smoking detection with a small data approach

arXiv

引用

arXiv 2023年

作者： Lakatos, Róbert Pollner, Péter Hajdu, András Joó, Tamás Data-Driven Health Division of National Laboratory for Health Security Health Services Management Training Centre Semmelweis University 2 Kútvölgyi Road BudapestH-1125 Hungary Department of Data Science and Visualization University of Debrecen Faculty of Informatics 26 Kassai Road DebrecenH-4002 Hungary Doctoral School of Informatics University of Debrecen 26 Kassai Road DebrecenH-4002 Hungary Neumann Technology Platform Neumann Nonprofit Ltd. 8 Naphegy Square BudapestH-1016 Hungary

Introduction: Covert tobacco advertisements often raise regulatory measures. This paper presents that artificial intelligence, particularly deep learning, has great potential for detecting hidden advertising and allows unbiased, reproducible, and fair quantification of tobacco-related media content. Methods: We propose an integrated text and image processing model based on deep learning, generative methods, and human reinforcement, which can detect smoking cases in both textual and visual formats, even with little available training data. Results: Our model can achieve 74% accuracy for images and 98% for text. Furthermore, our system integrates the possibility of expert intervention in the form of human reinforcement. Conclusions: Using the pre-trained multimodal, image, and text processing models available through deep learning makes it possible to detect smoking in different media even with few training data. © 2023, CC BY.

关键词： Deep learning

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A generative artificial intelligence framework based on a molecular diffusion model for the design of metal–organic frameworks for carbon capture

arXiv

引用

arXiv 2023年

作者： Park, Hyun Yan, Xiaoli Zhu, Ruijie Huerta, Eliu A. Chaudhuri, Santanu Cooper, Donny Foster, Ian Tajkhorshid, Emad Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Visualization Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Metal–organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m molg−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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