IoT Platform Business Model for Innovative Management Systems

Tripathi

2021

J Innov Entrep

Self Cite

Artificial intelligence is the ecosphere’s prevalent and most comprehensive general acquaintance common-sense cognitive engine. The artificial intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on the top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in the digital data fluid through the coordination, fueling business enhancements over phases. In this business model, the business will safekeep a recurrent subscription. This paper endeavors to emphasize on the preventative side of the use of AI and machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics. We acme the strategic implications and innovations with analytics. We explore the derivations of data-driven insights, models, and visualizations.

Section: Resultsmentioning

confidence: 99%

Section: The Convergence Of Ai With Other Emerging Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

AI business model: an integrative business approach

Tripathi

2021

J Innov Entrep

Self Cite

“…The IoE platform connect the cohesive corporeal effects accessible through connected API and machine learning enabled devices. This IoE platform ecosystem make accessible us with amenities to associate the strategies and things for a machine to machine and machine to man communication (Spiekermann, 2019, 2020a).…”

Section: Internet Of Everything's Communication Taxonomymentioning

confidence: 99%

Design and Implementation of Internet of Everything’s Business Platform Ecosystem

Tripathi

Singh

et al. 2021

Preprint

Self Cite

Internet of Everything plays a vital role for the business and industry. Internet of everything’s is an assembly of technologies, strategies over the network strategies and connections which required a set of protocols, data server storage strategy, infrastructure mechanism, integration of all technology, and communication protocols with all the connecting devices. Internet of everything’s (IoE) is a diversified and distributed interconnection of the platform strategies, heterogeneous devices through man and machines over the Internet. Business tycoons are continuously shifting their business model to innovative business model with internet technology-based features. The IoE based model able to generate and capture huge amounts of data in nano seconds. Industrialist using this data driven approach in their businesses to generate huge number of profits from their business. IoE based data driven approach learn from existing patterns and solutions to predict the future behavior and analytics of business. There is however, very little attention has given on platform business models from an Internet of Everything viewpoint and data driven approaches. In this study we examine up-to-date landscape of IoE business platform ecosystem along with 5G and 6G technology scope for business to how they can be integrated to work on IoE platform ecosystem. JEL Classification: M10, M12, M13, M15, M16, M21, M31, M37.

“…On the other hand, Median filter [ 47 ] belongs to class of non-linear filter where pixels inside a selected window are ranked and filtering is performed based on pixel ordering [ 42 ]. This involves filtering parameter as size and shape of filtering window rather then weighted mask.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images

Sharma

2022

Multimed Tools Appl

The outbreak of novel coronavirus (COVID-19) disease has infected more than 135.6 million people globally. For its early diagnosis, researchers consider chest X-ray examinations as a standard screening technique in addition to RT-PCR test. Majority of research work till date focused only on application of deep learning approaches that is relevant but lacking in better pre-processing of CXR images. Towards this direction, this study aims to explore cumulative effects of image denoising and enhancement approaches on the performance of deep learning approaches. Regarding pre-processing, suitable methods for X-ray images, Histogram equalization, CLAHE and gamma correction have been tested individually and along with adaptive median filter, median filter, total variation filter and gaussian denoising filters. Proposed study compared eleven combinations in exploration of most coherent approach in greedy manner. For more robust analysis, we compared ten CNN architectures for performance evaluation with and without enhancement approaches. These models are InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, Vgg19, NASNetMobile, ResNet101, DenseNet121, DenseNet169, DenseNet201. These models are trained in 4-way (COVID-19 pneumonia vs Viral vs Bacterial pneumonia vs Normal) and 3-way classification scenario (COVID-19 vs Pneumonia vs Normal) on two benchmark datasets. The proposed methodology determines with TVF + Gamma, models achieve higher classification accuracy and sensitivity. In 4-way classification MobileNet with TVF + Gamma achieves top accuracy of 93.25% with 1.91% improvement in accuracy score, COVID-19 sensitivity of 98.72% and F1-score of 92.14%. In 3-way classification our DenseNet201 with TVF + Gamma gains accuracy of 91.10% with improvement of 1.47%, COVID-19 sensitivity of 100% and F1-score of 91.09%. Proposed study concludes that deep learning modes with gamma correction and TVF + Gamma has superior performance compared to state-of-the-art models. This not only minimizes overlapping between COVID-19 and virus pneumonia but advantageous in time required to converge best possible results.