Magnetic Induction-based Human Activity Recognition (MI-HAR)

Golestani, Negar; Moghaddam, Mahta

doi:10.1109/apusncursinrsm.2019.8888468

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Naive Bayes is the best algorithm for classification in machine learning. Based on the Naive Bayes eorem, each object's likelihood was estimated, allowing it to foretell its appearance in any given class [11,[41][42][43][44][45][46][47][48][49][50]. Naive Bayes theorem states the following:…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Mushtaq

Ramzan

Ali

et al. 2022

Mobile Information Systems

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Diabetes mellitus is a hyperglycemia-like chronic condition that is a troublesome disease. It is estimated that, according to the growing morbidity, by 2040, the world will cross 642 million diabetic patients. This means that each one of the ten adults will be diabetes-affected. Diabetes can also lead to other illnesses such as heart attacks, kidney damage, and even blindness. The prediction of diabetes in advance motivates us to develop a machine learning-based model. A dataset was obtained from the online repository for this work. The obtained dataset was imbalanced. An imbalanced dataset presents a challenge that is needed to be balanced for prediction using multiple machine learning like Tomek and SMOTE. These techniques remove necessary outliers that are incomplete in the provided dataset. These outliers are also managed using the IQR method. Additionally, this research employed a two-stage model selection methodology. In the first stage, logistic regression, Support Vector Machine, k-nearest neighbors, gradient boost, Naive Bayes, and Random Forests were applied to determine the efficiency of prediction based on patients’ preconditioning. At this stage, Random Forest was found to be the best with an accuracy of 80.7% after applying SMOTE oversampling technique to balance the dataset. In the second stage, three better-performing models were used by utilizing a voting algorithm. The results were encouraging, and the model obtained 82.0% accuracy with the default dataset and 81.7% accuracy with the balanced dataset. Naive Bayes Theorem, Gradient Boosting Classifier, and Random Forest were used as inputs to the voting algorithm.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Mushtaq

Ramzan

Ali

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

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“…However, there are many conflicting requirements that must be taken into account when a system operating around the human body is designed. One of the main challenges in this context is related to power management, since the attenuation of electromagnetic waves propagating around lossy media is high [5]. In the last years, HAR has been managed following two antithetical approaches: the first one concerning external equipment and the second one characterized by the employment of wearable sensors.…”

Section: Human Activity Recognitionmentioning

confidence: 99%

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

Succetti¹,

Rosato²,

Luzio³

et al. 2022

PIER

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“…The proposed [8] model was maintained similarly as Magnetic Induction recognition model used, where progressively direct models are continually loved in case they have (almost) indistinguishable ability to acknowledge when stood out from progressively complex techniques. The degree of this work is to apply the present advancement for enveloping information applications, for instance, in remote patient checking and smart conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The precision of K-NN calculation can be degraded within the sight of commotion or improper highlights. In design recognition, K-NN is a strategy for masterminding objects dependent on closest preparing highlights in the component space [8], [13], [19] Order and Regression Tree Classification and Regression Tree calculation characterizes an example as showed by social events of various models with near properties. During setting up, the arrangement data is continually disengaged into littler subsets.…”

Section: K-nearest Neighbourmentioning

confidence: 99%

Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset

Arshad

Jaskani

Sabri³

et al. 2022

EAI Endorsed Transactions on Internet of Things

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The cell phone is assuming a crucial job in present day life. It offers types of assistance and applications, for example, location tracking, medical applications, and human activity examination. All android smartphones have motion sensors i.e. Accelerometer, gyroscope, in order to detect motion of a user in a very precise way. In early conditions, committed sensors were utilized for activity acknowledgment. Different techniques are developed for distinguishing normal or human activities scenes in the crowd by processing the video or an image. A novel KNN-SVM human activity detection method is proposed to detect human activities in the UCI dataset for complex multi-process physical activities. Model trained with machine learning algorithms to capture the temporal dependency, normal sequences with high dimension is uniformly utilized to train the model to discriminate each activity. In the classification process, 2 different efficient classifiers are applied to identify the types of human activities in the UCI dataset. Support Vector Machine and K-Nearest Neighbour are applied in the proposed method for the classification. The efficiency of each classifiers is about 85% to 87%. The classification efficiency is comparable with existing literature after applying the majority decision in these classification techniques.

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Magnetic Induction-based Human Activity Recognition (MI-HAR)

Cited by 8 publications

References 6 publications

Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset

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