Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection. © 2001-2012 IEEE.

Analyzing the Barriers to Building a 3-D Printing Enabled Local Medical Supply Chain Ecosystem

Building a local supply chain requires separating the regions and creating alliances with local partners and customers, resulting in a new business model. In local supply chains, the factory procures material, parts, and preassembled elements from local suppliers and sells the final products to local customers. Three-dimensional printing (3DP) has the potential to enable a more local, globally connected, and efficient supply chain through reduced inventory and transportation costs transforming the make-to-stock to the make-on-demand production cycle. In this study, we use an integrated Interpretive Structural Model and Decision-making Trial and Evaluation Laboratory technique to explore and assess the challenges faced by the 3DP companies to become enabling partners in the localized supply chains. The scope of the study, which was limited to 3DP of medical parts and components, identified that regulatory compliance, stringent quality standards, and lack of design expertise are significant barriers to developing localized three-dimensional printing ecosystems. Furthermore, the study identified immediate support from the local government, the high collaboration between the stakeholders, and the need for change in business approach as the key drivers for developing 3DP-enabled localized supply chain ecosystems. IEEE

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, as well as the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices as well as cloud computing services, as well as basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, as well as generate multivariate data to provide just-in-time healthcare services. In this paper, we present a novel collaborative disease detection system based on IoMT as well as captured image data. The system can be based on intelligent agents, where each and every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared to baseline solutions for disease detection. IEEE

Digital technologies and circular economy practices: vital enablers to support sustainable and resilient supply chain management in the post-COVID-19 era

Purpose: The COVID-19 pandemic has caused major disruptions and revealed the fragilities in supply chains. This crisis has re-opened the debate on supply chain resilience and sustainability. This paper aims to investigate distinct impacts of COVID-19 on supply chains. It identifies both short- and medium-to-long-term measures taken to mitigate the different effects of the pandemic and highlights potential transformations and their impacts on supply chain sustainability and resilience. Design/methodology/approach: To address the purpose of the study, a qualitative research approach based on case studies and semi-structured interviews with 15 practitioners from various supply chain types and sectors was conducted. Studied organizations included necessary and non-necessary supply chain sectors, which are differently impacted by the COVID-19 pandemic. Findings: This study reveals five main challenges facing supply chains during COVID-19, including uncertain demand and supply, suppliers' concentration in specific regions, globalized supply chains, reduced visibility in the supply network, and limited supplier capacity. To help mitigate these challenges and develop both sustainability and resilience, this paper identifies some mitigating actions focusing on the promotion of the health and wellbeing of employees and supply chain stabilization. Further, in the post-COVID era, sustainable and resilient supply chains should consider regionalization of the supply chain, diversification of the supply network, agility, collaboration, visibility, and transparency;and should accelerate the use of smart technologies and circular economy practices as dynamic capabilities to improve supply chain resilience and sustainability. Originality/value: This study contributes to exploring the sustainability- and resilience-related challenges posed by the COVID-19 pandemic. Its findings can be used by researchers and supply chains decision-makers to limit disruptions and improve responsiveness, resilience, sustainability, and restoration of supply chains. The results support benchmarking through sharing of the best practices and organizations can also integrate the different capabilities discussed in this study into the processes of selection and auditing of their suppliers. © 2022, Anass Cherrafi, Andrea Chiarini, Amine Belhadi, Jamal El Baz and Abla Chaouni Benabdellah.

Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis

This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform's user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.