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Hybrid Diet Recommender System Using Machine learning Technique 22nd

Hybrid Diet Recommender System Using Machine Learning Techni...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Vignesh, N. Bhuvaneswari, S. Kotecha, Ketan Subramaniyaswamy, V. School of Computing SASTRA Deemed University Thanjavur613401 India Pune India

ISBN: (纸本)9783031274084

Obesity is a dangerous epidemic worldwide and is the root cause of many diseases. It is difficult for people to have the same diet with an optimized calorie intake as it becomes monotonous and boring. It will be much better if a dynamic diet can be generated depending upon the calories burnt by a person and their current Body Mass Index (BMI). The active diet planner could provide a person with some change regarding the food consumed and, at the same time, regulate the calorie intake depending upon the user’s requirements. Previously proposed models are either focused only on one aspect of the nutritional information of food or on presenting a diet for a specific issue which is presently facing by user. The proposed system utilizes more balanced approach that focuses on most of the nutritional features of food, and can recommend different foods to a user depending on their BMI. The fat, carbohydrate, calorie, and protein content of food and the BMI of the user are considered while preparing the diet chart. K-means clustering is used to cluster food of similar nutritional content, and a random forest classifier is then used to build the model to recommend a diet for the user. The result of the system cannot be compared with a standard metric. Still, some of the factors that influence the performance of the diet recommender system include the truthfulness of the user while providing information to the design and the accuracy at which the parameters for the model had been set. The advantage of the system comes from the fact that the user has more options to choose from within their suitable range. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Motion Based Real-Time Siamese Multiple Object Tracker Model 2nd

Motion Based Real-Time Siamese Multiple Object Tracker Model

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2nd international conference on Cybersecurity and Evolutionary data engineering, ICCEDE 2022

作者： Kanaujia, Vishal Kumar Yadav, Satya Prakash Mishra, Himanshu Kumar, Awadhesh de Albuquerque, Victor Hugo C. Computer Science and Engineering-Data Science Department ABES Engineering College Uttar Pradesh Ghaziabad India 201306 Greater Noida India CE Fortaleza Brazil Department of CSE-APEX Chandigarh University Punjab Gharuan Mohali India Department of Computer Science and Engineering Kamala Nehru In-Stitute of Technology Sultanpur—Kadipur Rd Uttar Pradesh Sultanpur228118 India Department of Teleinformatics Engineering Federal University of Ceará CE Fortaleza Brazil

ISBN: (纸本)9789819950799

Motion multiple object tracker model is the objective of the object detection and automated monitoring of the scene for suspicious activity incidents on an ongoing basis. An algorithm tracks the motion of an object in order to predict or anticipate where it will be in relation to its trajectory. The identification method of moving objects using digital image processing techniques in a video series is known as video sequence object detection. The most common tracking method is video surveillance, which is also used in video control systems, intelligent road systems, intrusion monitoring, and airport safety. The tracker has to perform object matching from one edge to the next to get the desired result in the image area. The Identification of object is mainly carried out by the removal of frontal areas. The proposed methodology outlines the conditions under which a particular method yields the most effective results and indicates the location of detected objects for surveillance purposes. This article aims to develop appropriate image processing and computer vision algorithms involving multiple input video sequences and perturbing detection rate study that measures the forward pixel detection rate of background models for different color contrasts for use in road flow and monitoring. This algorithm uses a method that takes into account the pixel intensities of the image, but the results vary in terms of speed, memory requirements to provide accurate results in these situations, and significantly underestimate performance in all cases. intelligent Transport Systems (ITS) have become more relevant around the world in today's overcrowded transport network and address persistent issues such as transport mobility and safety, in particular in metropolitan areas. Its main objectives are to improve the safety, transport comfort, and efficiency of road traffic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Object detection

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Investigating the Extended Metacommunication Template: How a semiotic tool may encourage reflective ethical practice in the development of machine learning systems 22

Investigating the Extended Metacommunication Template: How a...

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22nd Brazilian Symposium on Human Factorsin Computing Systems, IHC 2023

作者： Barbosa, Gabriel Diniz Junqueira Nunes, José Luiz De Souza, Clarisse Sieckenius Barbosa, Simone Diniz Junqueira Department of Informatics PUC-Rio Rio de Janeiro Rio de Janeiro Brazil Department of Informatics PUC-Rio FGV Direito Rio Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798400717154

In this paper we present a qualitative study based on speculative design sessions that investigate how developers may use semiotic engineering's Extended Metacommunication Template EMT) to engage in reflective ethical practice of software development in a Machine learning context. The template consists of a series of guiding questions about the development process, split into analysis, design, implementation, and evaluation phases. In the speculative design sessions, participants were presented with design briefs and asked to answer the questions while speculating about their design. Through 8 interviews resulting in 12 hours of recordings, we conducted a thematic analysis looking for salient themes. This process resulted in a codebook with 62 codes, structured around the main entities involved in the machine learning development process: data, System, Developers, Users, and Stakeholders;as well as 5 themes that aid us in understanding the potential impacts of the template's use. Our analysis indicates that the EMT shows promise for encouraging reflective ethical practice through linguistic approaches that highlight developers' agency over the system and its consequences for users and stakeholders. Further study is still necessary into how this tool may be incorporated into real-world software development processes. © 2023 ACM.

关键词： Machine learning

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learning High-dimensional Associations for Nonalcoholic Fatty Liver Disease Diagnosis Prediction 19

Learning High-dimensional Associations for Nonalcoholic Fatt...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Zhijin Cai, Bing Yang, Wen Zhang, Peisong Huang, Yaohui Tang, Jinmo Jimei University College of Computer Engineering Xiamen China Fujian University of Traditional Chinese Medicine Xiamen Hospital of Traditional Chinese Medicine Fuzhou China Jimei University School of Science Xiamen China Guangxi Minzu University College of Electronic Information Nanning China

ISBN: (纸本)9798350346558

Nonalcoholic Fatty Liver Disease (NAFLD) is a major liver disease worldwide, and NAFLD diagnosis is the clinical foundation before healthcare strategies. NAFLD diagnosis predictive methods provide a promising way for intelligent diagnosis. Previous diagnoses mainly relied on liver biopsy and imaging, which are unsuitable for large-scale rapid screening. However, the observed quantitative data are high-dimensional and have missing values. To alleviate this issue, the stacking multi-scaled convolutional neural network (SMCNN) is proposed to predict NAFLD diagnosis. Firstly, the inputs are normalized and transformed. Secondly, the inputs are represented as multi-scaled feature maps. Finally, the scaled feature maps are stacked to connect with outputs. Several experiments have been done to validate twelve methods on a real dataset. The proposed SMCNN outperforms eleven methods in terms of five metrics. © 2022 IEEE.

关键词： Forecasting

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learning Inter-Lingual Document Representations via Concept Compression 22nd

Learning Inter-Lingual Document Representations via Concept ...

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22nd international conference on intelligent data engineering and automated learning, IDEAL 2021

作者： Lenz, Marc Tashu, Tsegaye Misikir Horváth, Tomáš Faculty of Informatics Department of Data Science and Engineering Telekom Innovation Laboratories ELTE - Eötvös Loránd University Pázmány Péter Sétány Budapest1117 Hungary College of Informatics Wollo University Kombolcha Institute of Technology Kombolcha208 Ethiopia Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia

ISBN: (纸本)9783030916077

In this work, we proposed a novel approach to derive inter-lingual document representations. The introduced methods aim to enhance the quality of content-based Multilingual Document Recommendation and information retrieval Systems. The main idea centers around creating inter-lingual representations by using mappings to align monolingual representation spaces. According to the experimental results carried out on JRC-Acquis and EU bookshop multilingual corpora, the proposed concept compression approach has outperformed the traditional cross-lingual retrieval and recommendations methods. © 2021, Springer Nature Switzerland AG.

关键词： Information retrieval systems

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Research on Automatic Extraction Strategy of Text Knowledge Graph Based on Gephi

Research on Automatic Extraction Strategy of Text Knowledge ...

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Mechatronics, IoT and Industrial Informatics (ICMIII), international conference on

作者： Ren Shuaitao Kong Chuang Wang Hongwei Zhou Hua College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

This paper aims to explore an automated strategy for extracting text knowledge graphs using the Python programming language and the Gephi visualization tool. We extract structured knowledge from the professional book "OPERATING SYSTEM CONCEPTS—NINTH EDITION" and build a knowledge graph. The paper elaborates on the entire process from data preprocessing, knowledge point extraction and storage, data transformation, relationship data extraction to the construction of the final graph data. It demonstrates how to efficiently convert complex text materials into easily analyzable graph forms through Gephi, providing a reference direction for simplifying complex text knowledge and related research work.

关键词： Machine learning algorithms Accuracy Mechatronics Operating systems Oceans Knowledge graphs data mining

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Robust I/O-Compute Concurrency for Machine learning Pipelines in Constrained Cyber-Physical Devices 2021

Robust I/O-Compute Concurrency for Machine Learning Pipeline...

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22nd ACM SIGPLAN/SIGBED international conference on Languages, Compilers, and Tools for Embedded Systems (LCTES)

作者： Poroor, Jayaraj Lal, Akash Ghanta, Sandesh Amrita Vishwa Vidyapeetham Sch Engn Dept Comp Sci Amritapuri India Microsoft Res Bangalore Karnataka India JIFFY Ai Bengaluru India Amazon Bangalore Karnataka India

ISBN: (纸本)9781450384728

Cyberphysical systems have numerous industrial and commercial applications. Such systems are often built using low-resource devices that gather and process data, using machine-learning (ML) models, to make intelligent decisions and provide value to users. Programming such low-resource devices with an impoverished system runtime is often challenging. This paper presents a new domain-specific language called PiCon for programming ML pipelines in low-resource devices. PiCon allows safe I/O-compute concurrency, ruling out a large class of errors, while providing a simple and sequential coding abstraction to the programmer. PiCon compiles to C code and easily interfaces with existing C/C++ code. Furthermore, the generated code does not rely on multi-threading support or dynamic memory allocation, dramatically reducing its footprint on the device. We present experience porting two real-world ML applications that demonstrate simplification in programmability, in addition to several safe-by-construction guarantees.

关键词： Embedded systems Constrained devices IoT Machine learning Cyberphysical systems

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Research on the automated Monitoring Platform of Prefabricated Buildings Based on BIM

Research on the Automated Monitoring Platform of Prefabricat...

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Electrical, Automation and Computer engineering (ICEACE), international conference on

作者： Jingqiu Guo Juan Pan Chongqing College of Architecture and Technology China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This project aims to improve the efficiency and quality of engineering construction management and build a prefabricated construction monitoring system based on BIM technology. The system mainly includes functions such as data collection and integration, interaction between BIM models and construction data, real-time monitoring and analysis, data compression and storage optimization. This paper integrates BIM technology with IoT devices and design an efficient data compression method to solve the bottleneck problems of data transmission and storage caused by massive BIM models, so as to achieve the monitoring and quality management of the prefabricated building construction process. Combined with computer vision, machine learning and other methods, the automatic detection of component assembly quality and defects is realized, and the intelligence and management efficiency of engineering construction are improved. The research results of this project will help improve the efficiency of BIM data transmission, optimize the construction progress, improve the construction quality, and provide new ideas for the information and intelligent management of China's construction.

关键词： Prefabricated construction Automation Accuracy data compression Real-time systems data models data communication Monitoring Optimization Load modeling

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TinyMLEdge: A Workflow for Deploying TinyML Models in Industrial Edge Devices

TinyMLEdge: A Workflow for Deploying TinyML Models in Indust...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Feng Xu School of Engineering and Design Technical University of Munich Munich Germany

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In the current machine learning landscape, numerous algorithms and new models continuously emerge, demon-strating impressive capabilities across various tasks. However, models specifically tailored for particular environments remain limited. Despite their powerful performance, models like YOLO encounter challenges in small object detection and require substantial adjustments for different datasets and tasks. Further-more, their extensive parameters make them difficult to deploy on edge devices. Addressing these issues, TinyMLEdge is proposed as a workflow designed to facilitate the deployment of embedded TinyML models in specialized industrial edge devices.

关键词： Performance evaluation YOLO Training Machine learning algorithms Image edge detection Tiny machine learning data models Haptic interfaces Informatics Optimization

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Long-Term Fine-grained Forecasts of Emergency Demand Using EMS Big data and Regional Mesh Population Estimates

Long-Term Fine-grained Forecasts of Emergency Demand Using E...

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international conference on Software engineering Research, Management and Applications (SERA)

作者： Masaki Kaneda Sinan Chen Masahide Nakamura Sachio Saiki Graduate School of Engineering Faculty of Engineering Kobe University Kobe Japan School of Data and Innovation Kochi University of Technology Kochi Japan

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

In recent years, Japan has grappled with a rapidly aging population, leading to pressing issues in emergency medical care and an uptick in ambulance services. Our research team, collaborating with the Kobe City Fire Department, aims to address this by devising a predictive model for strategic deployment of medical services. By analyzing extensive EMS data and population demographics, we aim to forecast long-term emergency transport demand accurately, bypassing machine learning algorithms. The results of the forecast revealed regional disparities in future emergency demand, providing indicators for optimal deployment of emergency resources and strategic planning for emergency services.

关键词： COVID-19 Accuracy Urban areas Medical services Pressing Aging Strategic planning

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