Providing machine learning (ML) based security in heterogeneous IoT networks including resource-constrained devices is a challenge because of the fact that conventional ML algorithms require heavy computations. Therefore, this paper, lightweight ProtoNN, CMSIS-NN, and Bonsai tree were evaluated by using performance metrics such as testing accuracy, precision, F1 score recall to test their class...

Machine learning (ML) and statistical techniques are key to transforming big data into actionable knowledge. In spite of the modern primacy of data, the complexity of existing ML algorithms is often overwhelming—many users do not understand the trade-offs and challenges of parameterizing and choosing between different learning techniques. Furthermore, existing scalable systems that support mach...

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OBJECTIVE The present study explores the discriminatory performance of existing and novel gold-standard-specific machine learning (GSS-ML) focused filter models (i.e., models built specifically for a retrieval task and a gold standard against which they are evaluated) and compares their performance to citation count and impact factors, and non-specific machine learning (NS-ML) models (i.e., mod...

Rural credit is one of the most critical inputs for farm production across globe. Despite so many advances in digitalization emerging and developing economies, still a large part society like small holders, rural youth, women farmers are untouched by mainstream banking transactions. Machine learning-based technology giving new hope to these individuals. However, it or non-banking institutions t...

Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well the existence affordable highly interoperable devices. The Wi-Fi community is currently deploying 6 developing 7, which will bring higher data rates, better multi-user multi-AP support, and, most importantly, ...

ML models are ubiquitous in real world applications and a constant focus of research. At the same time, community has started to realize importance protecting privacy training data. Differential Privacy (DP) become gold standard for making formal statements about data anonymization. However, while some adoption DP happened industry, attempts apply complex still few far between. The is hindered ...

We propose a new data-centric synchronization framework for carrying out of machine learning (ML) tasks in a distributed environment. Our framework exploits the iterative nature of ML algorithms and relaxes the application agnostic bulk synchronization parallel (BSP) paradigm that has previously been used for distributed machine learning. Data-centric synchronization complements function-centri...