Occupancy Detection in Room Using Sensor Data

• URL: http://arxiv.org/abs/2101.03616v1
• Date: Sun, 10 Jan 2021 19:53:57 GMT
• Title: Occupancy Detection in Room Using Sensor Data
• Authors: Mohammadhossein Toutiaee
• Abstract summary: This paper provides a solution to detect occupancy using sensor data by using and testing several variables. Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of Internet of Thing (IoT), and ubiquitous data collected every moment by either portable (smart phone) or fixed (sensor) devices, it is important to gain insights and meaningful information from the sensor data in context-aware computing environments. Many researches have been implemented by scientists in different fields, to analyze such data for the purpose of security, energy efficiency, building reliability and smart environments. One study, that many researchers are interested in, is to utilize Machine Learning techniques for occupancy detection where the aforementioned sensors gather information about the environment. This paper provides a solution to detect occupancy using sensor data by using and testing several variables. Additionally we show the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms is possible to determine the occupancy of indoor environments. Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared in this study.

Related papers

• Learning 3D Perception from Others' Predictions [64.09115694891679]
  We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area.
  arXiv  Detail & Related papers  (2024-10-03T16:31:28Z)
• The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology [10.81691411087626]
  In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. We have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems.
  arXiv  Detail & Related papers  (2024-04-17T13:00:52Z)
• Federated Learning on Edge Sensing Devices: A Review [0.0]
  Federated Learning (FL) is emerging as a solution to privacy, hardware, and connectivity limitations. We focus on the key FL principles, software frameworks, and testbeds. We also explore the current sensor technologies, properties of the sensing devices and sensing applications where FL is utilized.
  arXiv  Detail & Related papers  (2023-11-02T12:55:26Z)
• Environmental Sensor Placement with Convolutional Gaussian Neural Processes [65.13973319334625]
  It is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues.
  arXiv  Detail & Related papers  (2022-11-18T17:25:14Z)
• Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else? [93.91375268580806]
  Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications. By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
  arXiv  Detail & Related papers  (2021-11-09T13:30:34Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• Domain and Modality Gaps for LiDAR-based Person Detection on Mobile Robots [91.01747068273666]
  This paper studies existing LiDAR-based person detectors with a particular focus on mobile robot scenarios. Experiments revolve around the domain gap between driving and mobile robot scenarios, as well as the modality gap between 3D and 2D LiDAR sensors. Results provide practical insights into LiDAR-based person detection and facilitate informed decisions for relevant mobile robot designs and applications.
  arXiv  Detail & Related papers  (2021-06-21T16:35:49Z)
• On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks [25.838878314196375]
  We evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate. The final objective is to identify the optimal setup that would minimize the amount of data to be distributed over the channel.
  arXiv  Detail & Related papers  (2021-04-23T18:58:37Z)
• Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data [64.48836187884325]
  Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment.
  arXiv  Detail & Related papers  (2021-02-09T15:48:35Z)
• Self-Supervised Transformers for Activity Classification using Ambient Sensors [3.1829446824051195]
  This paper proposes a methodology to classify the activities of a resident within an ambient sensor based environment. We also propose a methodology to pre-train Transformers in a self-supervised manner, as a hybrid autoencoder-classifier model.
  arXiv  Detail & Related papers  (2020-11-22T20:46:25Z)
• Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review [0.0]
  Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, fault prevention, and industrial automation. We review state-of-the-art methods that may be employed to detect anomalies in the specific area of sensor systems.
  arXiv  Detail & Related papers  (2020-10-27T09:56:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.