scholarly journals COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

2021 ◽  
Vol 10 ◽  
pp. 146-160
Author(s):  
Swarnava Biswas ◽  
Debajit Sen ◽  
Dinesh Bhatia ◽  
Moumita Mukherjee ◽  
Keyword(s):  
Internet Of Things ◽  
Transfer Learning ◽  
Value Chain ◽  
Predictive Analytics ◽  
Decision Fusion ◽  
Process Time ◽  
Ensemble Model ◽  
Fusion Algorithm ◽  
Suggested Approach ◽  
First Time

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem.

scholarly journals Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ahmed I. Iskanderani ◽  
Ibrahim M. Mehedi ◽  
Abdulah Jeza Aljohani ◽  
Mohammad Shorfuzzaman ◽  
Farzana Akther ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Transfer Learning ◽  
Ensemble Model ◽  
Learning Models ◽  
X Ray ◽  
Proposed Model ◽  
Cloud Server ◽  
Medical Sensors ◽  
Chest X Ray

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

MONITORING AND PREDICTIVE ANALYTICS OF TECHNOLOGICAL EQUIPMENT ON THE BASED OF INDUSTRIAL INTERNET OF THINGS

2020 ◽  
pp. 7-12
Author(s):  
С.Л. Добрынин ◽  
В.Л. Бурковский
Keyword(s):  
Internet Of Things ◽  
Data Acquisition ◽  
Predictive Analytics ◽  
Actual State ◽  
Monitoring And Control ◽  
Industrial Internet Of Things ◽  
Machining Time ◽  
Industrial Internet ◽  
Acquisition Device ◽  
Data Acquisition Device

Произведен обзор технологий в рамках концепции четвертой промышленной революции, рассмотрены примеры реализации новых моделей управления технологическими процессами на базе промышленного интернета вещей. Описано техническое устройство основных подсистем системы мониторинга и контроля, служащей для повышения осведомленности о фактическом состоянии производственных ресурсов в особенности станков и аддитивного оборудования в режиме реального времени. Архитектура предлагаемой системы состоит из устройства сбора данных (УСД), реализующего быстрый и эффективный сбор данных от станков и шлюза, передающего ликвидную часть информации в облачное хранилище для дальнейшей обработки и анализа. Передача данных выполняется на двух уровнях: локально в цехе, с использованием беспроводной сенсорной сети (WSN) на базе стека протоколов ZigBee от устройства сбора данных к шлюзам и от шлюзов в облако с использованием интернет-протоколов. Разработан алгоритм инициализации протоколов связи между устройством сбора данных и шлюзом, а также алгоритм выявления неисправностей в сети. Расчет фактического времени обработки станочных подсистем позволяет более эффективно планировать профилактическое обслуживание вместо того, чтобы выполнять задачи обслуживания в фиксированные интервалы без учета времени использования оборудования We carried out a review of technologies within the framework of the concept of the fourth industrial revolution; we considered examples of the implementation of new models of process control based on the industrial Internet of things. We described the technical structure of the main subsystems of the monitoring and control system to increase awareness of the actual state of production resources in particular machine tools and additive equipment in real time. The architecture of the proposed system consists of a data acquisition device (DAD) that implements fast and efficient data collection from machines and a gateway that transfers the liquid part of information to the cloud storage for further processing and analysis. We carried out the data transmission at two levels, locally in the workshop, using a wireless sensor network (WSN) based on ZigBee protocol stack from the data acquisition device to the gateways and from the gateways to the cloud using Internet protocols. An algorithm was developed for initializing communication protocols between a data acquisition device and a gateway, as well as an algorithm for detecting network malfunctions. Calculating the actual machining time of machine subsystems allows us to more efficiently scheduling preventive maintenance rather than performing maintenance tasks at fixed intervals without considering equipment usage

scholarly journals Cloud Manufacturing, Internet of Things-Assisted Manufacturing and 3D Printing Technology: Reliable Tools for Sustainable Construction

2021 ◽  
Vol 13 (13) ◽  
pp. 7327
Author(s):  
Rajesh Singh ◽  
Anita Gehlot ◽  
Shaik Vaseem Akram ◽  
Lovi Raj Gupta ◽  
Manoj Kumar Jena ◽  
...  
Keyword(s):  
3D Printing ◽  
Internet Of Things ◽  
Materials Science ◽  
Predictive Analytics ◽  
Residential Buildings ◽  
Three Dimensional ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
Sustainable Construction ◽  
Access Technology

The United Nations (UN) 2030 agenda on sustainable development goals (SDGs) encourages us to implement sustainable infrastructure and services for confronting challenges such as large energy consumption, solid waste generation, depletion of water resources and emission of greenhouse gases in the construction industry. Therefore, to overcome challenges and establishing sustainable construction, there is a requirement to integrate information technology with innovative manufacturing processes and materials science. Moreover, the wide implementation of three-dimensional printing (3DP) technology in constructing monuments, artistic objects, and residential buildings has gained attention. The integration of the Internet of Things (IoT), cloud manufacturing (CM), and 3DP allows us to digitalize the construction for providing reliable and digitalized features to the users. In this review article, we discuss the opportunities and challenges of implementing the IoT, CM, and 3D printing (3DP) technologies in building constructions for achieving sustainability. The recent convergence research of cloud development and 3D printing (3DP) are being explored in the article by categorizing them into multiple sections including 3D printing resource access technology, 3D printing cloud platform (3D–PCP) service architectures, 3D printing service optimized configuration technology, 3D printing service evaluation technology, and 3D service control and monitoring technology. This paper also examines and analyzes the limitations of existing research and, moreover, the article provides key recommendations such as automation with robotics, predictive analytics in 3DP, eco-friendly 3DP, and 5G technology-based IoT-based CM for future enhancements.

scholarly journals A muscle synergies-based movements detection approach for recognition of the wrist movements

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Aida Masoumdoost ◽  
Reza Saadatyar ◽  
Hamid Reza Kobravi
Keyword(s):  
Decision Fusion ◽  
Classification Performance ◽  
Muscle Synergy ◽  
Fusion Algorithm ◽  
Main Research ◽  
Main Challenge ◽  
Detection Approach ◽  
Average Accuracy ◽  
Bayesian Fuzzy Clustering ◽  
Research Challenge

Abstract Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively.

Decision fusion algorithm for target tracking in forward-looking infrared imagery

2004 ◽  
Author(s):  
Amer Dawoud ◽  
Mohammad S. Alam ◽  
Abdullah Bal ◽  
Chey H. Loo
Keyword(s):  
Target Tracking ◽  
Decision Fusion ◽  
Infrared Imagery ◽  
Fusion Algorithm ◽  
Forward Looking

Accounting of gross exports and tracing foreign values in the global pharmaceutical value chain

2018 ◽  
Vol 22 (4) ◽  
pp. 348-363
Author(s):  
Chang-soo Lee ◽  
Mikyung Yun
Keyword(s):  
Value Chain ◽  
Decomposition Method ◽  
Design Methodology ◽  
Bilateral Trade ◽  
Value Chains ◽  
Vertical Specialization ◽  
Content Type ◽  
Second Tier ◽  
First Time ◽  
European Regional

Purpose The purpose of this paper is to document for the first time the vertical specialization structure of the global pharmaceutical value chain. Design/methodology/approach The paper adopts Wang et al.’s (2013) gross exports decomposition method to trace foreign values in bilateral trade between major pharmaceutical producers, using the 2014 WIOT database. Findings The paper shows that as in other sectoral value chains, the pharmaceutical value chain is heavily regional. The paper identifies a strong European regional value chain, and a less intensive, Asian regional value chain. Korea is positioned in the middle of the Asian value chain, and is connected to the European regional value chain as a second-tier supplier. Originality/value The paper documents the vertical specialization structure of the global pharmaceutical value chain through gross exports decomposition method, making use of the World Input–Output Table Database 2014 which disaggregates pharmaceuticals in its industry classification for the first time.

The future of the Internet of Things: toward heterarchical ecosystems and service business models

2018 ◽  
Vol 33 (6) ◽  
pp. 749-767 ◽  
Author(s):  
Seppo Leminen ◽  
Mervi Rajahonka ◽  
Mika Westerlund ◽  
Robert Wendelin
Keyword(s):  
Internet Of Things ◽  
Business Model ◽  
Value Chain ◽  
Large Scale ◽  
Business Models ◽  
The Internet ◽  
Content Type ◽  
Industry Collaboration ◽  
New Business ◽  
The Internet Of Things

Purpose This study aims to understand their emergence and types of business models in the Internet of Things (IoT) ecosystems. Design/methodology/approach The paper builds upon a systematic literature review of IoT ecosystems and business models to construct a conceptual framework on IoT business models, and uses qualitative research methods to analyze seven industry cases. Findings The study identifies four types of IoT business models: value chain efficiency, industry collaboration, horizontal market and platform. Moreover, it discusses three evolutionary paths of new business model emergence: opening up the ecosystem for industry collaboration, replicating the solution in multiple services and return to closed ecosystem as technology matures. Research limitations/implications Identifying business models in rapidly evolving fields such as the IoT based on a small number of case studies may result in biased findings compared to large-scale surveys and globally distributed samples. However, it provides more thorough interpretations. Practical implications The study provides a framework for analyzing the types and emergence of IoT business models, and forwards the concept of “value design” as an ecosystem business model. Originality/value This paper identifies four archetypical IoT business models based on a novel framework that is independent of any specific industry, and argues that IoT business models follow an evolutionary path from closed to open, and reversely to closed ecosystems, and the value created in the networks of organizations and things will be shareable value rather than exchange value.

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