A Comparative Study for Temperature Prediction by Machine Learning and Deep Learning

Zhao, Heng; Chen, Yixing

doi:10.1109/ic-c57619.2023.00020

Cited by 2 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Concurrently, the representation with subspace structure can enhance the interpretability of deep learning models and mitigate the risk of model collapse 61 . Here, we use the self-expression matrix learned from the subspace to reconstruct the low-dimensional representations of the positive views through a one-step linearization 44 and to obtain the representations of the microenvironments that contain the same domain tissue. Meanwhile, we provide a post-processing tool that could mask the self-expression matrix based on the magnitude of self-expression values 62 .…”

Section: /28 Discussionmentioning

confidence: 99%

“…Once the self-expression coefficient matrix C is obtained, a reconstructed representation containing local spatial information can be computed, i.e., Z 1 = Ĥ1 C, Z 2 = Ĥ2 C (the process can be considered as linearization 44 ). Given that spots within the same subspace tend to belong to the same domain, the reconstructed representation encapsulates local information specific to that domain.…”

Section: /28mentioning

confidence: 99%

See 1 more Smart Citation

Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning

Gui,

Li,

2024

Preprint

View full text Add to dashboard Cite

Spatial transcriptomics (ST) technologies have emerged as an effective tool to identify the spatial architecture of the tissue, facilitating a comprehensive understanding of organ function and tissue microenvironment. Spatial domain identification is the first and most critical step in ST data analysis, which requires thoughtful utilization of tissue microenvironment and morphological priors. To this end, we propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and subspace module to accurately distinguish different spatial domains by capturing tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological images prior, preserving information crucial for the clustering task. Experimental results on 8 ST datasets from 5 different platforms show that GRAS4T outperforms five state-of-the-art competing methods in spatial domain identification. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.

show abstract

Section: /28 Discussionmentioning

confidence: 99%

Section: /28mentioning

confidence: 99%

Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning

Gui,

Li,

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The K-NN algorithm [9], [10] relies solely on the provided dataset and a constant parameter, denoted as K. The subsequent steps elucidate the workings of the K-NN algorithm:…”

Section: K-nearest Neighbors (K-nn)mentioning

confidence: 99%

IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

Hajdarevic

2024

TEM Journal

View full text Add to dashboard Cite

This paper presents a system that is able to detect physical intrusion in a specific space based on temperature and humidity change. This specific space was housing hardware components important for information security management infrastructure. Presented system is able to predict that two spaces are connected and that there is a physical breach in protected space. The presented prediction approach involves identifying patterns in historical data, where the subsequent outcomes are already known in advance, and validating these patterns using more recent data. System is implemented using k-Nearest Neighbours, Random Forest, and Support Vector Machine algorithms in Python programming language on Raspberry Pi. Real observed data to predict if specific temperature and humidity indicates intrusion were used. This approach can be used to detect intrusions in the room or in other closed space. More specifically thermal equilibrium phenomenon between two spaces after barrier between them are opened was monitored. Through process of supervised learning using labelled data, system was able to detect intrusion by using k-nearest neighbours, random forest, and support vector machine with different accuracy. Presented model shows better results using k-nearest neighbours and support vector machine with accuracy of 100% compared to random forest with accuracy of 95%. The system is low cost because of cheap Raspberry Pi controller and sensors.

show abstract

A Comparative Study for Temperature Prediction by Machine Learning and Deep Learning

Cited by 2 publications

References 25 publications

Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning

Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning

IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

Contact Info

Product

Resources

About