The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia ...healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a ...substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality in terms of noise and metal artifacts by adding Gaussian noise. Our CNN training results reveal 94.64% accuracy as compared to other state-of-the-art methods. We used SHAP to ensure consistency and local accuracy for interpretation as Shapley values examine all future predictions applying all possible combinations of inputs. In contrast, LIME constructs sparse linear models around each prediction to illustrate how the model operates in the immediate area. Our emphasis for this study is interpretability and high accuracy, which is critical for realising disparities in predictive performance, helpful in developing trust, and essential in integration into clinical practice. The proposed method has a vast clinical application that could potentially be used for mass screening in resource-constraint countries.
The concept of Internet of Things has silently existed since the late nineteenth century but in the current decade expectations and excitement has peaked. However not many have understood the ...profound change that it can usher in.
How big this change can be and how it can transform our working!!
This book aims to bring in this realization with illustrative and practical case studies with comprehensive concepts. From beginners to practitioners in the field of academics or industry, it serves as a comprehensive yet easy to comprehend source of information on the multiple facets of IoT.
Simplistic but comprehensive introduction of the facets of primarily the industrial IoT
Practical adoption cases explaining the Core technology stack and business applications
Comprehensive view of current technologies which complete the IoT delivery ecosystem, followed by overview of IoT enabled new business models.
Realistic view of how industrial firms can evolve into the next stage of maturity along with determinants influencing this transformation since manufacturing is envisioned to be a key segment to adopt and benefit from IoT.
Detailed analysis of IoT benefits for the universal triad- energy management, logistics optimization and distribution channel management.
A full-fledged case study on Adoption of Green manufacturing using IoT.
Real world example of gauging End User perception using different models which is important for a successful adoption of IoT.
A futuristic visionary view of IoT as comprehended based on evolution of technology and platforms, and finally analysis of the extremely crucial concepts of security, privacy and governance.
1. Introduction to IoT. 2. Internet of Things Applications. 3. Current Technolgies. 4. Business Models in IoT. 5. Manufacturing Automation. 6. Monitoring Energy Consumption. 7. Logistics Optimization. 8. Distribution Channel Management. 9. Green Manufacturing. 10. Industry and IoT Awareness. 11. Future and Internet of Things. 12. IoT Security and Privacy.
Ravi Ramakrishnan is an MBA from Faculty of Management Studies Delhi University and has further done a Post Graduate Diploma from AIMA in IT systems ,DOEACC A level certified and a Post Graduate Diploma in Operations Management from IGNOU after a Bachelors in Science. He is a Prince 2 certified professional, Microsoft Certified Professional and a Oracle Certified professional and has 20+ years of global experience and is an award winning Global CIO with a strong technical and managerial background and has done numerous global rollouts of Enterprise Information Systems –ERP/CRM/BI and M2M/Mobility and IoT solutions which have been widely acknowledged and awarded in different forums. He is currently pursuing his Doctorate in Information Technology Management with focus on IoT strategies and technologies and has published papers on IoT in Springer and IEEEXplore and book chapter on IoT in IGI Global. He is a Senior IEEE member and has implemented projects in US/Europe/Asia/Africa/Middle East across multiple cultures , industry domain verticals and technologies. His global awards include Computerworld Global 100 CIO honoree (Florida), IDG – CIO 100 India Award winner consecutively 2011,2013,2014,2015 , IDC – CIO 100 award winner 2014,2015 , Dataquest CIO award winner in Mobility and IoT categories ,Chief Information Security award winner CISO 2013,2014 , Oracle Best Implementation award winner 2016 , Innovative CIO award winner 2015,2016 , C-Change Awards CIOL 2015 ,Business World CIO 3.0 award winner 2016 , Information Week CIO and Business Impact Leader award 2015 and Information Week Edge Award 2014.
Dr. Loveleen Gaur is Professor in Amity International Business School. She is a PhD and M.Phil in Computer Applications and has done her M.C.A. She has more than 15 years of experience teaching, consulting, research and mentoring activities. She has honoured with Senior Women Educator & Scholar Award by National Foundation for Entrepreneurship Development on Women’s Day , “Sri Ram Award” by Delhi Management Association (DMA) and Distinguished Research Award: Allied Academies presented this award in Jacksonville, Florida and outstanding research contributor award by Amity International Business School. She has written numerous books with renowned publishers and has presented many research papers in international and national conferences. She is also serving as paper reviewer for many National and International Journals.
Deepfake technology uses auto-encoders and generative adversarial networks to replace or artificially construct fine-tuned faces, emotions, and sounds. Although there have been significant ...advancements in the identification of particular fake images, a reliable counterfeit face detector is still lacking, making it difficult to identify fake photos in situations with further compression, blurring, scaling, etc. Deep learning models resolve the research gap to correctly recognize phony images, whose objectionable content might encourage fraudulent activity and cause major problems. To reduce the gap and enlarge the fields of view of the network, we propose a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 ± 0.62, a test accuracy of 99.08 ± 0.64, and a validation accuracy of 99.30 ± 0.94. Additionally, we used 'SHapley Additive exPlanations (SHAP) ' as explainable AI (XAI) Shapely values to explain the results and interoperability visually by imposing the model into SHAP. The proposed model holds significant importance for being accepted by forensics and security experts because of its distinctive features and considerably higher accuracy than state-of-the-art methods.
COVID-19 has caused over 528 million infected cases and over 6.25 million deaths since its outbreak in 2019. The uncontrolled transmission of the SARS-CoV-2 virus has caused human suffering and the ...death of uncountable people. Despite the continuous effort by the researchers and laboratories, it has been difficult to develop reliable efficient and stable vaccines to fight against the rapidly evolving virus strains. Therefore, effectively preventing the transmission in the community and globally has remained an urgent task since its outbreak. To avoid the rapid spread of infection, we first need to identify the infected individuals and isolate them. Therefore, screening computed tomography (CT scan) and X-ray can better separate the COVID-19 infected patients from others. However, one of the main challenges is to accurately identify infection from a medical image. Even experienced radiologists often have failed to do it accurately. On the other hand, deep learning algorithms can tackle this task much easier, faster, and more accurately. In this research, we adopt the transfer learning method to identify the COVID-19 patients from normal individuals when there is an inadequacy of medical image data to save time by generating reliable results promptly. Furthermore, our model can perform both X-rays and CT scan. The experimental results found that the introduced model can achieve 99.59% accuracy for X-rays and 99.95% for CT scan images. In summary, the proposed method can effectively identify COVID-19 infected patients, could be a great way which will help to classify COVID-19 patients quickly and prevent the viral transmission in the community.
The study proposes a comprehensive model framework applying co-creation and satisfaction in a moderated mediated mechanism for improving customer Online Repurchase Intention (ORI) via Affective ...Experiential State (AES). A cross-sectional survey collected data from 542 Indian respondents who do online shopping. Using structural equation modeling the results reveal that mediation effects of Online Shopping Satisfaction (OSS) vary between high and low-level of customer co-creation. Results further reveal that AES is a very influential factor in affecting customers ORI and OSS mediates the effects of AES on customer repurchase intention.
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The Manufacturing sector in India is still not globally competitive (Deloitte, 2018) due to over emphasis on labour based production, lesser automation, legacy manufacturing assets and production ...plants, energy inefficient systems, usage of Information Technology to integrate the physical and cyber world for potential benefits which other countries have adopted (Rockstorm, 2018) is almost non-existent. According to sources adoption of IoT enabled Industry 4.0 can lead to decrease of production costs by up to 30%, logistics costs by 30% and quality management costs by up to 20% (Thomas, 2016). This study starts with a literature review of the factors that determine the manufacturing competitiveness index of India and how other developed countries are proceeding in these as compared to India. The study proceeds with identification of key determinants or constructs (abstract dimensions) of measuring the IoT maturity of any organization along with their relative weights and then identify the key factors (measurable) inside each construct. These constructs are hypothesized to be the building blocks of any IoT strategy to be adopted by Indian manufacturing concerns. By using these constructs definition, along with factors and possible measurable states, in which the factors can exist, an excel based IoT Maturity Self-Assessment tool has been developed which can be used by target Indian manufacturing organizations to identify their current state of preparedness. The objective of this study is to define an Internet of Things Maturity Model (IoT-MM) which can be used by organizations to assess their current status. Indian manufacturing has started adopting IoT but in a disoriented manner, with adoption being guided more by technology consideration than a holistic business drive and consideration encompassing the benefits of productivity, energy conservation and environmental management.
The study proposes a comprehensive model framework, Online Customer Experience-Attitude Behaviour Context model for online grocery retailing in a digital scenario. The research also studies the ...concept of value co-creation in a moderated mechanism. Data was collected from 526 respondents buying groceries online. Analytical Hierarchy Process, SPSS 23, AMOS 22 and PROCESS Macro were applied for further analysis, testing the hypothesis and model formulation. The results reveal that the antecedent's convenience, recovery, and delivery experience impacted the attitude significantly. The emerging concept of value co-creation influenced the overall relationship between the antecedent of Online Customer Experience and attitude but at lower level of value co-creation. Thus, suggesting that involving customers time to time in co-creating a delighting Online Customer Experience may be a good strategy for the online grocery retailers to elevate online customers' attitude and repurchase intention.
•This study proposes a conceptual framework, OCE - ABC Model for online grocery retailing.•Antecedent's convenience, recovery and delivery experience significantly impact the attitude.•The customers buying online are more involved with the retailers in co-creating value for improvising their delivery.•The emerging concept of value co-creation influences the overall relationship between antecedent of OCE and attitude.
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