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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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Fashion Recommendation System for E-Commerce using Deep Learning Algorithms

Fashion Recommendation System for E-Commerce using Deep Lear...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M S Arunkumar R Gopinath M Chandru R Suguna S Deepa V Omprasath Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchgode Department of Computer Science VET-Institute of Arts and Science (Co-Education) College Erode Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Technology - PG Kongu Engineering College Perundurai Erode Tamilnadu M.Sc (Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the increasingly competitive outlook of ecommerce, personalized recommendation systems have emerged as pivotal tools to enhance user experience and drive sales. This paper proposes a novel Fashion Recommendation System (FRS) leveraging deep learning techniques to offer personalized fashion suggestions to users. The system utilizes a combination of convolutional neural networks (CNNs) for image feature extraction. Through a comprehensive analysis of user interactions and preferences, the FRS generates tailored recommendations that align with individual style preferences and current trends. The work proposes a novel approach that utilizes the ResNet50 algorithm to analyze images of fashion items, extracting meaningful features that capture the essence of style, color, and texture. By training the ResNet50 model on vast datasets of fashion images, thereby enhancing its ability to make accurate recommendations. Through the integration of user preferences and historical data, this system can generate personalized fashion recommendations tailored to each user’s unique style and preferences, thus enhancing user satisfaction and engagement. By harnessing the power of deep learning and the ResNet50 algorithm, fashion retailers can offer personalized shopping experiences, increase customer retention, and drive sales.

关键词： Deep learning Training Image color analysis Feature extraction Market research User experience Electronic commerce Convolutional neural networks Recommender systems Residual neural networks

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Pretrained Model with CNN for Lung Cancer Identification and Classification

Pretrained Model with CNN for Lung Cancer Identification and...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Shaista Kanwal Sajid Ali Tahani Jaser Alahmadi Muhammad Assam Ghulam Jillani Ansari Uzair Aslam Bhatti Department of Information Sciences University of Education Lahore Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Software Engineering University of Science and Technology Bannu KPK Pakistan School of information and communication engineering Hainan University

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Lung cancer impacts both genders, and its early detection is key to lowering death rates. Current deep learning techniques for automated identification and classification of lung carcinoma face issues with interpretability, data variability handling, and broader applicability. In our research, we propose a novel method that merges a convolutional neural network (CNN) with a streamlined RESNET18 framework. This approach is designed for lung cancer detection and categorization from CT scans, involving image preprocessing like resizing and rescaling. It effectively differentiates between healthy and cancerous tissues, further classifying them as either Normal, Benign, or Malignant, including variants such as adenocarcinoma and squamous cell carcinoma. Our CNN+RESNET18 model boasts an impressive 99% accuracy, supporting radiologists in the intricate task of image analysis, reducing risks of incorrect or delayed diagnosis, and fostering prompt treatment for improved patient outcomes.

关键词： Accuracy Squamous cell carcinoma Computed tomography Scalability Lung cancer Mortality Streaming media Feature extraction Convolutional neural networks Artificial intelligence

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CCT5: A Code-Change-Oriented Pre-Trained Model

arXiv

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arXiv 2023年

作者： Lin, Bo Liu, Yepang Wang, Shangwen Xia, Xin Liu, Zhongxin Mao, Xiaoguang College of Computer Science National University of Defense Technology Changsha China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Zhejiang University Hangzhou China Key Laboratory of Software Engineering for Complex Systems China Research Institute of Trustworthy Autonoumous Systems United Kingdom

software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or identifying the defect-prone code changes. Considering that the cost of dealing with these tasks can account for a large proportion (typically around 70 percent) of the total development expenditure, automating such processes will significantly lighten the burdens of developers. To achieve such a target, existing approaches mainly rely on training deep learning models from scratch or fine-tuning existing pre-trained models on such tasks, both of which have weaknesses. Specifically, the former uses comparatively small-scale labelled data for training, making it difficult to learn and exploit the domain knowledge of programming language hidden in the large-amount unlabelled code in the wild;the latter is hard to fully leverage the learned knowledge of the pre-trained model, as existing pre-trained models are designed to encode a single code snippet rather than a code change (i.e., the difference between two code snippets). We propose to pre-train a model specially designed for code changes to better support developers in software maintenance. To this end, we first collect a large-scale dataset containing 1.5M+ pairwise data of code changes and commit messages. Based on these data, we curate five different tasks for pre-training, which equip the model with diverse domain knowledge about code changes. We fine-tune the pre-trained model, CCT5, on three widely-studied tasks incurred by code changes and two tasks specific to the code review process. Results show that CCT5 outperforms both conventional deep learning approaches and existing pre-trained models on these tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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Automatic State Matching Gaussian Process Ensemble for Wood Planer Control

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IFAC-PapersOnLine 2022年第10期55卷 625-630页

作者： Jean-Thomas Sexton Michael Morin Rémi Georges Foroogh Abasian Jonathan Gaudreault FORAC Research Consortium Department of Computer Science and Software Engineering Department of Operations and Decision Systems Université Laval Québec QC Canada FPInnovations Québec QC Canada

Wood planers are high speed sophisticated lumber finishing machines that are difficult to operate and for which the available data shows complex, non-linear patterns. We present a machine learning approach to build a control loop for an industrial wood planer. In order to predict the thickness of the outgoing boards with better accuracy than the industry standard whilst allowing dynamic planer adjustments, we use an ensemble of Gaussian Processes with a specialized weighting scheme we call Automatic State Matching. It reduces the prediction error by 39% compared to current industrial practice.

关键词： Smart manufacturing Machine learning Production control Gaussian process Industry 4.0

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A new transformation approach for complex systems modelling and simulation: Application to industrial control system

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International Journal of Simulation and Process Modelling 2021年第1期16卷 34-48页

作者： Seddari, Noureddine Boukelkoul, Sofiane Bouras, Abdelghani Belaoued, Mohamed Redjimi, Mohammed LICUS Laboratory Department of Computer Science University of 20 August 1955-Skikda Skikda21000 Algeria LIRE Laboratory Department of Software Technologies and Information Systems University of Abdelhamid Mehri-Constantine 2 Constantine25000 Algeria Department of Industrial Engineering Alfaisal University Riyadh12714 Saudi Arabia

Due to the increasing importance of complex systems, the problems of modelling and simulation (M&S) of these systems, their approaches and solutions have been studied extensively these last years. In this paper, we present a novel approach to modelling and simulation of discrete event dynamic systems (DEDS), based on the combination of discrete event systems specification (DEVS) and agent group role (AGR). In this study, the DEVS atomic models are transformed into agents and the DEVS coupled models into groups of agents. Thus, we provide a set of procedures and functions that allow the systematic transformation from DEVS into AGR models in order to be implemented in multi-agent platforms. The proposed approach was validated by a case study, an application in the water supply used in natural gas liquefaction process. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Water supply

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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systems of Signals Generating and Processing in the Field of on Board Communications

作者： Ivan Filianin Nikita Borisov Vladimir Bogatyrev Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia Department of Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation St. Petersburg Russia

ISBN: (数字)9798331532635

ISBN: (纸本)9798331532642

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it.

关键词： computer languages Multi-access edge computing Codes Computational modeling Machine learning computer architecture Complex systems Research and development

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Signal Attenuation Modeling and Simulation for 5G and Beyond P2P Wireless Communication Networks in FR2 Bands: Comparison within M-Region in Nigeria 2

Signal Attenuation Modeling and Simulation for 5G and Beyond...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Alozie, Emmanuel Faruk, Nasir Abdulkarim, Abubakar Usman, Aliyu D. Olayinka Imam-Fulani, Yusuf Garba, Salisu Adewole, Kayode S. Oloyede, Abdulkarim A. Sowande, Olugbenga A. Abdullahi Baba, Bashir Musa, Abdulwaheed Imoize, Agbotiname Lucky Adediran, Yinusa A. Olagunju, Hawau I. Taura, Lawan S. Sule Lamido University Department of Information Technology Kafin Hausa Nigeria Sule Lamido University Directorate of Information and Communication Technology Kafin Hausa Nigeria Ahmadu Bello University Department of Electrical Engineering Kafin Hausa Nigeria Kampala International University Department of Electrical and Telecommunications Engineering Kampala Uganda Ahmadu Bello University Department of Electronics and Telecommunication Engineering Zaria Nigeria University of Ilorin Department of Telecommunication Science Ilorin Nigeria Sule Lamido University Department of Software Engineering Kafin Hausa Nigeria University of Ilorin Department of Computer Science Ilorin Nigeria Sule Lamido University Department of Cyber Security Kafin Hausa Nigeria Kwara State University Department of Electrical and Computer Engineering Malete Nigeria University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Lagos Department of Electrical and Electronics Engineering Lagos Nigeria Ruhr University Institute of Digital Communication Department of Electrical Engineering and Information Technology Bochum Germany University of Ilorin Department of Electrical and Electronics Engineering Ilorin Nigeria Sule Lamido University Department of Physics Kafin Hausa Nigeria

ISBN: (纸本)9798350358834

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region. © 2023 IEEE.

关键词： Location

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