Combining Context Connectivity and Behavior Association to Develop an Indoor/Outdoor Context Detection Model With Smartphone Multisensor Fusion

Zhu, Feng; Chen, Weijie; Guo, Fei; Zhang, Xiaohong

doi:10.1109/jiot.2023.3293792

IEEE Internet Things J.

2024

DOI: 10.1109/jiot.2023.3293792

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Combining Context Connectivity and Behavior Association to Develop an Indoor/Outdoor Context Detection Model With Smartphone Multisensor Fusion

Feng Zhu

Weijie Chen

Fei Guo

et al.

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2024

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Cited by 3 publications

References 33 publications

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Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes

Khan,

Hooda,

Gaur

et al. 2024

Sci Rep

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This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1–3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

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Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes

Khan,

Hooda,

Gaur

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection

Bilal Akram Dastagir,

Tariq,

Han

2024

IEEE Access

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DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors

Dastagir,

Tariq,

Han

2024

Sensors

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Accurate indoor–outdoor detection (IOD) is essential for location-based services, context-aware computing, and mobile applications, as it enhances service relevance and precision. However, traditional IOD methods, which rely only on GPS data, often fail in indoor environments due to signal obstructions, while IMU data are unreliable on unseen data in real-time applications due to reduced generalizability. This study addresses this research gap by introducing the DeepIOD framework, which leverages IMU sensor data, GPS, and light information to accurately classify environments as indoor or outdoor. The framework preprocesses input data and employs multiple deep neural network models, combining outputs using an adaptive majority voting mechanism to ensure robust and reliable predictions. Experimental results evaluated on six unseen environments using a smartphone demonstrate that DeepIOD achieves significantly higher accuracy than methods using only IMU sensors. Our DeepIOD system achieves a remarkable accuracy rate of 98–99% with a transition time of less than 10 ms. This research concludes that DeepIOD offers a robust and reliable solution for indoor–outdoor classification with high generalizability, highlighting the importance of integrating diverse data sources to improve location-based services and other applications requiring precise environmental context awareness.

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Combining Context Connectivity and Behavior Association to Develop an Indoor/Outdoor Context Detection Model With Smartphone Multisensor Fusion

Cited by 3 publications

References 33 publications

Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes

Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes

TabCLR: Contrastive Learning Representation of Tabular Data Classification for Indoor-Outdoor Detection

DeepIOD: Towards A Context-Aware Indoor–Outdoor Detection Framework Using Smartphone Sensors

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