1,079 research outputs found
Experimentation and Analysis of Ensemble Deep Learning in IoT Applications
This paper presents an experimental study of Ensemble Deep Learning (DL) techniques for the analysis of time series data on IoT devices. We have shown in our earlier work that DL demonstrates superior performance compared to traditional machine learning techniques on fall detection applications due to the fact that important features in time series data can be learned and need not be determined manually by the domain expert. However, DL networks generally require large datasets for training. In the health care domain, such as the real-time smartwatch-based fall detection, there are no publicly available large annotated datasets that can be used for training, due to the nature of the problem (i.e. a fall is not a common event). Moreover, fall data is also inherently noisy since motions generated by the wrist-worn smartwatch can be mistaken for a fall. This paper explores combing DL (Recurrent Neural Network) with ensemble techniques (Stacking and AdaBoosting) using a fall detection application as a case study. We conducted a series of experiments using two different datasets of simulated falls for training various ensemble models. Our results show that an ensemble of deep learning models combined by the stacking ensemble technique, outperforms a single deep learning model trained on the same data samples, and thus, may be better suited for small-size datasets
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification
Classifiers that can be implemented on chip with minimal computational and
memory resources are essential for edge computing in emerging applications such
as medical and IoT devices. This paper introduces a machine learning model
based on oblique decision trees to enable resource-efficient classification on
a neural implant. By integrating model compression with probabilistic routing
and implementing cost-aware learning, our proposed model could significantly
reduce the memory and hardware cost compared to state-of-the-art models, while
maintaining the classification accuracy. We trained the resource-efficient
oblique tree with power-efficient regularization (ResOT-PE) on three neural
classification tasks to evaluate the performance, memory, and hardware
requirements. On seizure detection task, we were able to reduce the model size
by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of
boosted trees, using the intracranial EEG from 10 epilepsy patients. In a
second experiment, we tested the ResOT-PE model on tremor detection for
Parkinson's disease, using the local field potentials from 12 patients
implanted with a deep-brain stimulation (DBS) device. We achieved a comparable
classification performance as the state-of-the-art boosted tree ensemble, while
reducing the model size and feature extraction cost by 10.6X and 6.8X,
respectively. We also tested on a 6-class finger movement detection task using
ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature
computation cost by 5.1X. The proposed model can enable a low-power and
memory-efficient implementation of classifiers for real-time neurological
disease detection and motor decoding
The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning
The quality of air is closely linked with the life quality of humans,
plantations, and wildlife. It needs to be monitored and preserved continuously.
Transportations, industries, construction sites, generators, fireworks, and
waste burning have a major percentage in degrading the air quality. These
sources are required to be used in a safe and controlled manner. Using
traditional laboratory analysis or installing bulk and expensive models every
few miles is no longer efficient. Smart devices are needed for collecting and
analyzing air data. The quality of air depends on various factors, including
location, traffic, and time. Recent researches are using machine learning
algorithms, big data technologies, and the Internet of Things to propose a
stable and efficient model for the stated purpose. This review paper focuses on
studying and compiling recent research in this field and emphasizes the Data
sources, Monitoring, and Forecasting models. The main objective of this paper
is to provide the astuteness of the researches happening to improve the various
aspects of air polluting models. Further, it casts light on the various
research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers
Commun (2023
Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments
This paper presents a multifunctional interdisciplinary framework that makes
four scientific contributions towards the development of personalized ambient
assisted living, with a specific focus to address the different and dynamic
needs of the diverse aging population in the future of smart living
environments. First, it presents a probabilistic reasoning-based mathematical
approach to model all possible forms of user interactions for any activity
arising from the user diversity of multiple users in such environments. Second,
it presents a system that uses this approach with a machine learning method to
model individual user profiles and user-specific user interactions for
detecting the dynamic indoor location of each specific user. Third, to address
the need to develop highly accurate indoor localization systems for increased
trust, reliance, and seamless user acceptance, the framework introduces a novel
methodology where two boosting approaches Gradient Boosting and the AdaBoost
algorithm are integrated and used on a decision tree-based learning model to
perform indoor localization. Fourth, the framework introduces two novel
functionalities to provide semantic context to indoor localization in terms of
detecting each user's floor-specific location as well as tracking whether a
specific user was located inside or outside a given spatial region in a
multi-floor-based indoor setting. These novel functionalities of the proposed
framework were tested on a dataset of localization-related Big Data collected
from 18 different users who navigated in 3 buildings consisting of 5 floors and
254 indoor spatial regions. The results show that this approach of indoor
localization for personalized AAL that models each specific user always
achieves higher accuracy as compared to the traditional approach of modeling an
average user
A Hybridized- Logistic Regression and Deep Learning-based Approaches for Precise Anomaly Detection in Cloud
Anomaly Detection plays a pivot role in determining the abnormal behaviour in the cloud domain. The objective of the manuscript is to present two approaches for Precise Anomaly Detection Approaches by hybridizing RBM with LR and SVM models. The various phases in the present approach are (a) Data collection (b) Pre-processing and normalization; OneHot Encoder for converting categorical values to numerical values followed by encoding the binary features through normalization (c) training the data (d) Building the Feedforward Deep Belief Network (EDBN) using hybridizing Restricted Boltzmann Machine (RBM) with Logistic Regression (LR) and Support Vector Machine (SVM); In the first approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using LR model. In the later approach, RBM model is trained through unsupervised pre-training followed by fine-tuning using SVM model; both the approaches adopt unsupervised pre-training followed by supervised-fine-tuning operations (e) Model Evaluation using the significant parameters such as Precision, Recall, Accuracy, F1-score and Confusion Matrix. The experimental evaluations concluded the effective anomaly detection techniques by integrating the RBM with LR and SVM for capturing the intricate patterns and complex relationships among the data. The proposed approaches paves a path to improved anomaly detection technique, thereby enhancing the security features and anomaly monitoring systems across distinct domains
Cost aware Inference for IoT Devices
Networked embedded devices (IoTs) of limitedCPU, memory and power resources are revo-lutionizing data gathering, remote monitoringand planning in many consumer and businessapplications. Nevertheless, resource limita-tions place a significant burden on their ser-vice life and operation, warranting cost-awaremethods that are capable of distributivelyscreening redundancies in device informationand transmitting informative data. We pro-pose to train a decentralized gated networkthat, given an observed instance at test-time,allows for activation of select devices to trans-mit information to a central node, which thenperforms inference. We analyze our proposedgradient descent algorithm for Gaussian fea-tures and establish convergence guaranteesunder good initialization. We conduct exper-iments on a number of real-world datasetsarising in IoT applications and show that ourmodel results in over 1.5X service life withnegligible accuracy degradation relative to aperformance achievable by a neural network.http://proceedings.mlr.press/v89/zhu19d/zhu19d.pdfPublished versio
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