Integration of Artificial Intelligence (AI) and Machine Learning (ML) into Product Roadmap Planning

Khare, Pranav; Arora, Sahil; Gupta, Sandeep

doi:10.1109/icecsp61809.2024.10698502

2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP) 2024

DOI: 10.1109/icecsp61809.2024.10698502

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Integration of Artificial Intelligence (AI) and Machine Learning (ML) into Product Roadmap Planning

Pranav Khare,

Sahil Arora,

Sandeep Gupta

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2024

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Cited by 2 publications

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Integrating Smart IoT and AI-Enhanced Systems for Predictive Diagnostics Disease in Healthcare

Suhag Pandya

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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A developing technology known as the IoT allows for the connectivity of seemingly innocuous gadgets to the web. The IoT has linked millions of sensors and smart devices, which has transformed the healthcare sector. IoT devices driven by AI can identify diabetes by continuously gathering accurate glucose values. This study addresses the critical need for early and accurate diabetes detection using advanced ML algorithms, leveraging the Pima Indian Diabetes Dataset. An accuracy of diagnosing diabetes and non-diabetic cases is evaluated and compared in this study using models such as CNN, XG-Boost, DT, and SVM. Show that the model may improve diagnostic accuracy and facilitate early intervention by conducting a thorough performance evaluation utilising confusion matrices and measures like recall, accuracy, precision, and F1-score. CNN demonstrates superior accuracy of 99% with high precision of 98% and recall rates of 99.9%, positioning it as the most effective model for diabetes detection in this study. The findings underscore the feasibility of DL approaches in healthcare data analysis, contributing to improved healthcare outcomes in diabetes diagnostics.

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Integrating Smart IoT and AI-Enhanced Systems for Predictive Diagnostics Disease in Healthcare

Suhag Pandya

2024

Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol

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Optimizing Kubernetes Network Performance: A Study of Container Network Interfaces and System Tuning Profiles

Chippagiri

2024

ejtas

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The rapid development of cloud computing and big data has led to containers becoming the top choice for application deployment platforms due to their lightweight and flexible nature. Deploying, maintaining, and scaling containerized applications across dispersed environments has never been easier than with Kubernetes, a top container orchestration platform. Central to Kubernetes' functionality is its networking model, which connects containerized workloads seamlessly. This study explores Kubernetes network performance, emphasizing the role of Container Network Interfaces (CNIs) like Cilium, Flannel, Calico, and Antrea. Each CNI plugin is analyzed based on its architecture, functionality, and suitability for various application scenarios, highlighting trade-offs between simplicity, security, throughput, and scalability. Additionally, system tuning profiles and Kubernetes' default networking solutions are discussed to optimize communication between containers. The findings provide insights into selecting and configuring CNIs to enhance the performance, reliability, and security of Kubernetes clusters, offering guidance for deploying modern, network-intensive applications.

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Integration of Artificial Intelligence (AI) and Machine Learning (ML) into Product Roadmap Planning

Cited by 2 publications

References 7 publications

Integrating Smart IoT and AI-Enhanced Systems for Predictive Diagnostics Disease in Healthcare

Integrating Smart IoT and AI-Enhanced Systems for Predictive Diagnostics Disease in Healthcare

Optimizing Kubernetes Network Performance: A Study of Container Network Interfaces and System Tuning Profiles

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