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2nd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Anastasi, Sara Madonna, Marianna Monica, Luigi Italian Workers Compensat Author INAIL Dept Technol Innovat & Safety Equipment Prod & Se Via Roberto Ferruzzi 38-40 I-00143 Rome Italy Italian Workers Compensat Author INAIL Operat Terr Unit Res Certificat & Verificat Area Via Nuova Poggioreale I-80143 Naples Italy

The Artificial Intelligence (AI) and the machine learning (ML) is a rapidly evolving technology and up until recently has not been a subject of machinery safety. The purpose of this work is to evaluate how embedded artificial intelligence machine learning can affect the safety of machinery and machinery systems in the development of their applications. This work can be useful to machinery designers to develop their particular applications as it describes how the new hazards, associated with embedded AI ML, should be considered within the framework of the risk assessment process. The proposed study underlines the new dimension of complexity linked to artificial intelligence and machine learning that could lead to a revision of European legislation in terms of the introduction and/or modification of essential health and safety requirements (EHSR) in the machinery Directive, in order to guarantee safety levels at least equivalent to those currently achieved. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Safety of machinery Artificial Intelligence machine learning machinery Directive

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Automated machine learning-Based Gestational Monitoring Framework in Wearable Internet of Things Environment 2nd

Automated Machine Learning-Based Gestational Monitoring Fram...

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2nd international conference on Mechanical and Energy Technologies , ICMET 2021

作者： Nandini, K. Seetha, J. Jegajothi, B. Rajmohan, R. Nageswari, D. Department of Electronics and Instrumentation Engineering Easwari Engineering College Chennai India Department of Computer Science SRM Institute of Science and Technology SRMIST Bharathi Salai Ramapuram Campus Chennai600089 India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai India Department of Electronics and Communication Science Agurchand Manmull Jain College Meenambakkam Chennai India Department of Electrical and Electronics Engineering RMK College of Engineering and Technology Chennai India

ISBN: (纸本)9789811901072

Recently, Internet of Things (IoT) and wearable technologies have become popular in diverse areas, and smart health is an important application area. Specifically, IoT and wearable technologies find useful in linking different medicinal gadgets, sensors, and physicians in offering better healthcare services in remote areas. It results in enhanced patient safety, minimum healthcare cost, improved healthcare service accessibility, and high operational efficiency in the healthcare sector. At the same time, high-quality care during pregnancy is essential to determine the probable difficulties earlier and guarantee the healthiness of mother as well as fetus. Several research works have been existed in the literature to monitor the maternal lifetime. But they are developed for a particular health issue and based on short-term data collection approaches. Since maternal monitoring necessitates long-term services, this paper designs an automated machine learning-based gestational monitoring framework in wearable IoT environment. The goal of this paper is to derive a new improved salp swarm optimization (ISSO) with extreme learning machine (ELM), called ISSO-ELM model for continual maternal health monitoring. The ISSO-ELM model encompasses different sub-processes namely data acquisition, pre-processing, classification, and parameter optimization. The presented model enables IoT devices and wearables to gather healthcare data about pregnant women. Then, the gathered data are pre-processed to remove the unwanted noise. Besides, the ELM model is employed as a classifier to determine the occurrence of difficulties. Moreover, ISSO algorithm is applied as a parameter tuning technique to optimally adjust the parameters that exist in the ELM model. The extensive experimental analysis highlighted the enhanced performance of the ISSO-ELM model over the recent state-of-art methods with the precision, recall, and accuracy of 94.00, 88.00, and 90.07%. © 2023, The Author(s), under exclusiv

关键词： Internet of things

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Comparing Deep learning and Conventional machine learning for Outcome Prediction of Head and Neck Cancer in PET/CT 2nd

Comparing Deep Learning and Conventional Machine Learning fo...

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2021

作者： Huynh, Bao-Ngoc Ren, Jintao Groendahl, Aurora Rosvoll Tomic, Oliver Korreman, Stine Sofia Futsaether, Cecilia Marie Faculty of Science and Technology Norwegian University of Life Sciences Universitetstunet 3 Ås1433 Norway Department of Clinical Medicine Aarhus University Nordre Ringgade 1 Aarhus8000 Denmark

ISBN: (纸本)9783030982522

Prediction of cancer treatment outcomes based on baseline patient characteristics is a challenging but necessary step towards more personalized treatments with the aim of increased survival and quality of life. The HEad and neCK TumOR Segmentation Challenge (HECKTOR) 2021 comprises two major tasks: auto-segmentation of GTVt in FDG-PET/CT images and outcome prediction for oropharyngeal head and neck cancer patients. The present study compared a deep learning regressor utilizing PET/CT images to conventional machine learning methods using clinical factors and radiomics features for the patient outcome prediction task. With a concordance index of 0.64, the conventional machine learning approach trained on clinical factors had the best test performance. Team: Aarhus_Oslo. © 2022, Springer Nature Switzerland AG.

关键词： Forecasting

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A Comprehensive Analysis of machine learning Methods for Air Pollution Forecasting 2

A Comprehensive Analysis of Machine Learning Methods for Air...

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2nd international conference on Innovative and Creative Information Technology, ICITech 2021

作者： Mohith, J. Kulshrestha, Divij Jothi, K.R. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9781728197463

Air pollution is a threat that all urban municipalities across the globe are trying to tackle. In India, air pollution is the fifth major cause of death, leading to around 2 million deaths per year, according to the World Health Organization. The ability to accurately predict air pollution levels in a region would give authorities the chance to take proactive measures, preventing the exposure of citizens to toxic pollutants and avoiding accidents and damage to property caused by smog. In this paper, we forecast the level of Particulate Matter 2.5 (PM2.5) for multiple urban cities in India using various machine learning algorithms built upon historical data. This data includes meteorological parameters such as temperature, wind speed, humidity, and the pollutant levels leading up to that given date/time. Based on the performance of the forecasting models, we perform a comparative analysis of each model and derive key insights. © 2021 IEEE.

关键词： machine learning

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Design and Development of Manga Mobile application Recommender System 2

Design and Development of Manga Mobile Application Recommend...

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2nd international conference on Emerging Smart Technologies and applications, eSmarTA 2022

作者： Saleh, Abdulrazak Yahya Syafiqah Hailmi, Nurul FSKPM Faculty Sarawak Malaysia

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

Reading an e-book has become common in modern society due to its convenience. Manga, another sort of e-book, has developed a devoted following over the years. As a result, when selling products online, most businesses have a recommendation system in place. The majority of websites, however, are not made with the customer in mind;rather, the corporations' force add-ons sell to clients by making recommendations. As a result, our research primarily focused on a machine learning strategy known as a clustering-based method to address this constraint. The user's preferences have been taken into account by the clustering-based method. The agile methodology has been used in our study. A mobile application has been used to assess how effective this strategy is. Following evaluation, the utility of the system is assessed at 85.71 percent, followed by satisfaction at 93.20 percent, ease of use at 88.53 percent, ease of learning at 95.83 percent, and usefulness at 85.71 percent. Overall, the system performed well in the usability assessment with a score of 85.08 percent. © 2022 IEEE.

关键词： Recommender systems

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Artificial Intelligence Use in Human Resources Management: Strategy and Operation's Impact 2

Artificial Intelligence Use in Human Resources Management: S...

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2nd IEEE international conference on Pattern Recognition and machine learning, PRML 2021

作者： Achchab, Said Temsamani, Yassine Khallouk University Mohammed v Engineering School of Information Technology and System Analysis Rabat Morocco

ISBN: (纸本)9781665443838

HR technology leaders foresee AI's growing role in a variety of areas, such as aiding recruitment, improving compliance, augmenting training, streamlining onboarding and more. New artificial intelligence technologies that automate and augment the workforce could be the key to solving some of the thorny issues and increased demands for HR to accomplish more with fewer resources. The article presents the application of artificial intelligence in Human Resources, the challenges and how to use AI to support and develop a successful workforce. © 2021 IEEE.

关键词： machine learning

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Job running time prediction algorithm based on classification and ensemble learning 2

Job running time prediction algorithm based on classificatio...

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2nd international conference on Algorithms, Microchips, and Network applications, AMNA 2023

作者： Kong, Xubo Zhang, Dandan Zheng, Yu Ji, Qing College of Computer and Artificial Intelligence Zhengzhou University Henan Zhengzhou450000 China

ISBN: (纸本)9781510664791

Backfill scheduling is a common scheduling strategy in high-performance computing systems that allows priority execution of low-priority jobs to make better use of available resources. Job running time is an important parameter that affects the performance of backfill scheduling algorithm. However, in order to avoid job killing due to lack of time, the running time requested by users is often several times higher than the actual running time, resulting in a certain degree of resource waste. In order to improve resource utilization, a new job running time prediction algorithm is proposed by combining classification and ensemble learning methods. The algorithm first classifies the historical job set according to the application type, then uses Jaccard coefficient to calculate the similarity between the jobs, and further classifies the jobs. At the same time, different integration models are constructed for the jobs of different application types. New jobs are categorized and the class's integration model is used to predict the running time of the new job. The algorithm was tested on the historical job data of the National Supercomputing Center Kunshan, Hefei Advanced Computing Center and "Wuzhen Light" supercomputing Center, and compared with GA-sim algorithm and IRPA algorithm. The experimental results show that compared with the IRPA algorithm, the average absolute error of the algorithm is improved by 60% on the three data sets on average. Compared with the GA-sim algorithm, the average prediction accuracy of the algorithm is improved by 20% on the three data sets on average. Through the in-depth analysis of the experimental results, the amplification method for the low estimation of long and short jobs is given. © 2023 SPIE.

关键词： learning systems

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Finite Element-Based machine learning Method to Predict Breast Displacement during Running

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AATCC JOURNAL OF RESEARCH 2021年第1_suppl期8卷 69-74页

作者： Liang, Ruixin Yip, Joanne Yu, Winnie Chen, Lihua Lau, Newman Hong Kong Polytech Univ Hong Kong Peoples R China Beijing Univ Technol Beijing Peoples R China

This paper presents an effective method to simulate the dynamic deformation of the breasts when a sports bra is worn during physical activity. A subject-specific finite element (FE) model of a female subject is established, and the accuracy of the material coefficients of the model is analyzed. An FE model of the sports bra is also built based on a commercially-available compression sports bra with a vest style. Then, an FE contact model between the body and bra is developed and validated, and the results applied to train a neural network model for predicting breast displacement based on bra straps with different tensile moduli. In this study, a four-layer neural network with a backpropagation algorithm (a Levenberg-Marquardt learning algorithm) is used. A comparison of the FE and machine learning results shows that machine learning can well predict the dynamic displacement of the breasts in a more time-efficient and convenient manner.

关键词： Breast Support computer Vision Neural Network Simulation Sports Bra

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Data Processing in Foggy Computing Environments for machine learning 2

Data Processing in Foggy Computing Environments for Machine ...

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2nd international conference on Neural Networks and Neurotechnologies, NeuroNT 2021

作者： Subbotin, Alexey N. Saint-Petersburg Electrotechnical University 'Leti' Saint Petersburg Russia

ISBN: (纸本)9781665445344

This article discusses the processing of video information using cloud technologies and foggy environments. Features of mobile devices and embedded computers are touched upon. Data processing processes for video analytics are considered. Presented are data processing technologies, transmission methods and storage methods. The task of reducing processing delays in cloud systems is set and a solution to the problem is proposed using the example of filtering negative and criminal content on the site. An application for monitoring and controlling filtration has been developed. Specific examples are given. © 2021 IEEE.

关键词： machine learning

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machine learning in the Development of Integrated Tourist Destinations 2

Machine Learning in the Development of Integrated Tourist De...

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2nd international conference on computer Science and Engineering, IC2SE 2021

作者： Muhammad, Abulwafa Saputra, Dhio Universitas Putra Indonesia YPTK Padang Faculty of Computer Science Padang Indonesia

ISBN: (纸本)9781665400459

Cultural diversity, history and natural charm make West Sumatra worthy of being a leading tourist destination. Culinary diversity is the main attraction in the tourism sector in West Sumatra Province. Along with these developments, various tourist destinations have sprung up including shopping tourism destinations, underwater tourism, theme tours, and historical tours, but the grouping of tourist destinations based on variables has not been optimally carried out. This research was conducted to classify various tourist destinations based on the variables in tourism, namely mileage, transportation facilities, accommodation facilities, restaurant facilities, information facilities, number of accommodations and travel time of tourist destinations. The grouping of tourist destinations uses one of the k-means machine learning clustering algorithms, where the iteration process is only up to the 2nd iteration process. This study resulted in a group of 21 integrated tourist destinations, and 48 destinations that were not integrated. The results of this study can be used as a reference for tourists and also travel service actors in compiling tour packages. © 2021 IEEE.

关键词： machine learning

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