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2020 IEEE International Conference on Communications, Computing, Cybersecurity, and informatics, CCCI 2020

作者： Abubakar, Hamza Obaidat, M.S. Gupta, Aaryan Bhattacharya, Pronaya Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad Gujarat India College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates King Abdullah II School of Information Technology University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing10008 China

ISBN: (纸本)9781728120355

In this era, the agile mindset has innovated the traditional software engineering (SE) process through the integration of DevOps flow engines, scrum iterations, and automation of continuous integration (CI) and continuous deployment (CD) cycles. However, the CI/CD integration requires manual code-revisions and refactoring at large scales. Recently, machine-learning (ML) is employed in SE that allows legacy codes to be highly dynamic, less coupling in related modules, automatic code versioning, and refactoring, with less coupling among related modules. However, over time, ML models tend to become bulky, with increasing monotonic losses during model training. To address this, SE techniques like code revisions are employed over ML codes to allow low-order training losses, that enables seamless and precise workflow structures. Motivated from the aforementioned discussions, the paper presents a systematic review of the close interplay of SE and ML and possible interactions in different applications. Suitable research questions and case studies are presented for possible adoption scenarios that depict the close ML-SE interplay share with each other, with the concluding remarks. The paper forms useful insights for ML engineers, data science practitioners, and SE quality estimators towards the building of efficient and scalable software solutions. © 2020 IEEE.

关键词： Machine learning

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Green IoT using UAVs in B5G networks: A review of applications and strategies

arXiv

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arXiv 2021年

作者： Alsamhi, Saeed H. Afghah, Fatemeh Sahal, Radhya Hawbani, Ammar Al-Qaness, A.A. Lee, B. Guizani, Mohsen Software Research Institute Athlone Institute of Technology Athlone Ireland IBB University Ibb Yemen School of Informatics Computing and Cyber Systems Northern Arizona University United States Hudaidah University Hudaida Yemen School of Computer Science and Technology University of Science and Technology of China Hefei China State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Department of Computer Science and Engineering Qatar University Qatar

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented. © 2021, CC BY.

关键词： Energy efficiency

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Assessment of PM2.5-associated mortality burden among the elderly: Insights into demographic, socio-economic, and geographic factors

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Air Quality, Atmosphere & Health 2024年第7期17卷 1535-1545页

作者： Sharma, Ayushi Hsiao, Hsi-yu Liu, Jia-Yi Lung, Shih-Chun Candice Su, Huey-Jen Shen, Ching-Fen Chen, Nai-Tzu Wu, Pei-Chih Lin, Cheng-Yu Liang, Sheng-Fu Cheng, Tain-Junn Chan, Ta-Chien Tsay, Yaw-Shyan Chung, Hsin-Ying Wang, Yu-Chun Department of Environmental Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Civil Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Research Center for Environmental Changes Academia Sinica Taipei City Taiwan Department of Environmental and Occupational Health National Cheng Kung University Tainan City Taiwan National Cheng Kung University Hospital Pediatrics Office Tainan City Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan City Taiwan Sleep Medicine Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering & Institute of Medical Informatics National Cheng Kung University Tainan City Taiwan Department of Neurology and Occupational Medicine Chi Mei Medical Center Tainan City Taiwan School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan Department of Architecture National Cheng Kung University Tainan City Taiwan Department of Plant Industry National Pingtung University of Science and Technology Tainan City Taiwan

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality.

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Inter-mobile-device distance estimation using network localization algorithms for digital contact logging applications

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Smart Health 2021年 19卷

作者： Clark, Lillian Papalia, Alan Carvalho, Jônata Tyska Mastrostefano, Luca Krishnamachari, Bhaskar Ming Hsieh Department of Electrical and Computer Engineering University of Southern California United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Informatics and Statistics (INE) Federal University of Santa Catarina Brazil Covid Community Alert United Kingdom

Mobile applications are being developed for automated logging of contacts via Bluetooth to help scale up digital contact tracing efforts in the context of the ongoing COVID-19 pandemic. A useful component of such applications is inter-device distance estimation, which can be formulated as a network localization problem. We survey several approaches and evaluate the performance of each on real and simulated Bluetooth Low Energy (BLE) measurement datasets with respect to both distance estimate accuracy and the proximity detection problem. We investigate the effects of obstructions like pockets, differences between device models, and the environment (i.e. indoors or outdoors) on performance. We conclude that while direct estimation can provide the best proximity detection when Received Signal Strength Indicator (RSSI) measurements are available, network localization algorithms like Isomap, Local Linear Embedding, and the spring model outperform direct estimation in the presence of missing or very noisy measurements. The spring model consistently achieves the best distance estimation accuracy. © 2020 Elsevier Inc.

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Towards living manufacturing systems

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Procedia CIRP 2020年 93卷 323-328页

作者： László Monostori József Váncza Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

The impact of the forth industrial revolution on the social and natural environment is considered significant and far-reaching, even though the interactions of the human, natural and manufactured assets are less understood, extremely complex and unpredictable. The paper discusses how present days’ – so-called cyber-physical – manufacturing systems operate in the fabrics of society and the natural environment. It is underlined that they show and – to an increasing extent – will show features and capabilities reminiscent of living beings and organizations. This view helps to understand how manufacturing interacts with the ecosystem and suggests resolutions for the dilemma of competitive and sustainable manufacturing.

关键词： Manufacturing sustainability life

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Using Fitness Dependent Optimizer for Training Multi-layer Perceptron

arXiv

引用

arXiv 2022年

作者： Abbas, Dosti Kh. Rashid, Tarik A. Abdalla, Karmand H. Bacanin, Nebojsa Alsadoon, Abeer Faculty of Engineering Soran University Kurdistan Iraq Computer Science and Engineering Department University of Kurdistan Hewler Kurdistan Iraq Education and Psychology Department College of Education University of Raparin Kurdistan Iraq Faculty of Informatics and Computing Singidunum University Belgrade11000 Serbia School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Australia Sydney Australia Kent Institute Australia Information Technology Department Sydney Australia

This study presents a novel training algorithm depending upon the recently proposed Fitness Dependent Optimizer (FDO). The stability of this algorithm has been verified and performance-proofed in both the exploration and exploitation stages using some standard measurements. This influenced our target to gauge the performance of the algorithm in training multilayer perceptron neural networks (MLP). This study combines FDO with MLP (codename FDO-MLP) for optimizing weights and biases to predict outcomes of students. This study can improve the learning system in terms of the educational background of students besides increasing their achievements. The experimental results of this approach are affirmed by comparing with the Back-Propagation algorithm (BP) and some evolutionary models such as FDO with cascade MLP (FDO-CMLP), Grey Wolf Optimizer (GWO) combined with MLP (GWO-MLP), modified GWO combined with MLP (MGWO-MLP), GWO with cascade MLP (GWO-CMLP), and modified GWO with cascade MLP (MGWO-CMLP). The qualitative and quantitative results prove that the proposed approach using FDO as a trainer can outperform the other approaches using different trainers on the dataset in terms of convergence speed and local optima avoidance. The proposed FDO-MLP approach classifies with a rate of 0.97. © 2022, CC BY.

关键词： Multilayer neural networks

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Evaluation of the Montenegrin Academic Digital Innovation Hub

Evaluation of the Montenegrin Academic Digital Innovation Hu...

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International Scientific-Professional Conference on Information technology (IT)

作者： Luka Laković Peter A. Kara Ivana Ognjanović Laszlo Bokor Ramo Šendelj Christoph Reich Milovan Roganović Emmanouil Zoulias Jevto Eraković Tanja Radusinović Nada Rakočević Aniko Simon Faculty for Information Systems and Technologies University of Donja Gorica 81000 Podgorica Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 1111 Budapest Hungary Department of Computer Science School of Computer Science and Mathematics Wireless Multimedia and Networking Research Group Faculty of Science Engineering and Computing Kingston University London KT1 2EE UK Humanistic Studies University of Donja Gorica 81000 Podgorica Montenegro Institute of Data Science Cloud Computing and IT Security Furtwangen University 78120 Furtwangen Germany Clinic for Neurology Clinical Centre of Montenegro 81000 Podgorica Montenegro Faculty of Nursing School of Health Sciences National and Kapodistrian University of Athens 11527 Goudi Greece Chamber of Economy of Montenegro 81000 Podgorica Montenegro Sigma Technology 1093 Budapest Hungary

In the era of global digitalization, digital innovation hubs have become essential, as they function as transforming forces. In this paper, we provide an analysis of the Montenegrin Academic Digital Innovation Hub by applying multi-criteria qualitative evaluation over several criteria: process flow of fully digitized operations of the Hub (with aspects of the innovation life-cycle, ethics, and security), resource management, strategic goals, and feasibility. The paper also proposes a model of comprehensive resource management via the perspective of smart specialization priorities of Montenegro.

关键词： Technological innovation Ethics Resource management Security Information technology

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Combining attentional cnn and gru networks for ocean current prediction based on hf radar observations 8

Combining attentional cnn and gru networks for ocean current...

引用

8th International Conference on Computing and Pattern Recognition, ICCPR 2019

作者： Thongniran, Nathachai Jitkajornwanich, Kulsawasd Lawawirojwong, Siam Srestasathiern, Panu Vateekul, Peerapon Department of Computer Engineering Faculty of Engineering Chulalongkorn University Thailand Department of Computer Science Faculty of Science King Mongkut's Institute of Technology Ladkrabang Thailand GISTDA Academy Geo-Informatics and Space Technology Development Agency

ISBN: (纸本)9781450376570

Lately, CNN-GRU demonstrates the ability of deep learning techniques on ocean surface current prediction. Improvement of the current prediction model creates positive impact on variety of marine activities, such as search-and-rescue, disaster monitoring and power forecasting. Deep learning techniques was successfully deployed to improve model performance in many areas due to their ability to handle enormous amounts of information in a variety of inputs and their huge growth in recent years. Latest ocean current prediction employed a combination of two mature techniques, which are Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), to capture spatial and temporal characteristics of its nature. However, there is still room for improvement due to many modern techniques that still have not been employed, and domain knowledge in oceanic, such as lunar illumination, is not taken into account to improve prediction performance. This paper introduces the ocean surface prediction model that employs soft attention mechanism, transfer learning, and incorporation of domain knowledge inputs which are month number, lunar effect, and hour number. An experimental dataset from 2014 to 2016, provided by GISTDA, is collected by using high frequency (HF) radar stations located along the coastal Gulf of Thailand. The experiment compares an existing CNN-GRU and our proposed model. The result shows an improvement of the prediction model in terms of RMSE by 2.57%, and 3.44% on U and V components. © 2019 Association for Computing Machinery.

关键词： Forecasting

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AI based Technologies for International Space Station and Space Data

AI based Technologies for International Space Station and Sp...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Piyush Pant Anand Singh Rajawat S.B. Goyal Amol Potgantwar Pradeep Bedi Maria Simona Raboaca Neagu Bogdan Constantin Chaman Verma School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya Malaysia Sandip Institute of Technology and Research Center Sandip University Nashik India Deapartment of CSE Galgotias University Greater Noida India ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Department of Power Engineering Gheorghe Asachi” Technical University of Iasi Iasi Romania Faculty of Informatics University of Eötvös Loránd Budapest Hungary

ISBN: (纸本)9781665487351

Billions of galaxies, stars, solar systems, planets, and other undiscovered mysterious objects are present in the continuously expanding space. Humans took a giant step forward when the International Space Station was deployed into Earth's lower orbit for a better understanding of space. This research focuses of the Artificial Intelligence based technologies that are deployed in the International Space Station and potential models. The study proposes some Machine Learning models and technologies that could be deployed in the International Space Station to increase its efficiency and provide security to the crew. Powerful and trending Machine Learning/Deep Learning Algorithms like ANN and Clustering algorithms are suggested by the paper to get insights from the data gathered from the space and to promote the Industry Automation. A detailed explanation of the requirement, operation, and construction of NASA's “Robonauts” designed for the International Space Station is discussed in the research. The paper also put light on the ATLAS, an asteroid detecting system. The methods for providing medical aid to the crew, debris and its influence, analyzing data and extracting insight from space research data using machine learning are also highlighted. Investigation on how technologies used in space exploration could be used in the ISS to improve its performance and an overview of some of the existing AI-based technologies deployed in the International Space Station (ISS) is discussed.

关键词： Space vehicles Industries Training Earth Machine learning algorithms International Space Station Solar system

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Towards a Taxonomy of Industrial Challenges and Enabling Technologies in Industry 4.0

arXiv

引用

arXiv 2022年

作者： Figliè, Roberto Amadio, Riccardo Tyrovolas, Marios Stylios, Chrysostomos Pasko, Lukasz Stadnicka, Dorota Carreras-Coch, Anna Zaballos, Agustín Navarro, Joan Mazzei, Daniele The Computer Science Department University of Pisa Pisa56127 Italy The Department of Informatics and Telecommunications University of Ioannina Arta47150 Greece Industrial Systems Institute Athena Research Center Patras26504 Greece The Faculty of Mechanical Engineering and Aeronautics Rzeszów University of Technology Rzeszów35959 Poland Research Group in Internet Technologies & Storage Universitat Ramon Llull Barcelona08022 Spain

Today, one of the biggest challenges for digital transformation in the Industry 4.0 paradigm is the lack of mutual understanding between the academic and the industrial world. On the one hand, the industry fails to apply new technologies and innovations from scientific research. At the same time, academics struggle to find and focus on real-world applications for their developing technological solutions. Moreover, the increasing complexity of industrial challenges and technologies is widening this hiatus. To reduce this knowledge and communication gap, this article proposes a mixed approach of humanistic and engineering techniques applied to the technological and enterprise fields. The study’s results are represented by a taxonomy in which industrial challenges and I4.0-focused technologies are categorized and connected through academic and grey literature analysis. This taxonomy also formed the basis for creating a public web platform where industrial practitioners can identify candidate solutions for an industrial challenge. At the same time, from the educational perspective, the learning procedure can be supported since, through this tool, academics can identify real-world scenarios to integrate digital technologies’ teaching process. © 2022, CC BY-SA.

关键词： Industry 4.0

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