Multiple sensors-based kernel machine learning in smart environment

2017 ◽  
Vol 88 (1) ◽  
pp. 015006 ◽  
Author(s):  
Jun-Bao Li ◽  
Jeng-Shyang Pan
Keyword(s):  
Machine Learning ◽  
Smart Environment ◽  
Kernel Machine ◽  
Multiple Sensors

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

scholarly journals Survey on Stress Detection Using Multiple Sensors through Wearable Devices

2021 ◽  
Vol 10 (2) ◽  
pp. 787-790
Keyword(s):  
Machine Learning ◽  
Wearable Devices ◽  
Anxiety And Depression ◽  
Accuracy Score ◽  
Machine Learning Method ◽  
Stress Detection ◽  
Multiple Sensors ◽  
Machine Learning Model ◽  
Wide Range ◽  
Principle Objective

An Individual method of living on with a daily existence it directly influences on your overall health. Since stress is the significant infection of our human body. Like depression, heart attack and mental illness. WHO says “Globally, more than 264 million people of all ages suffer from depression.”[8]. Also the report says that most of the time people are stressed because of their work. 10.7% of People disorder with stress, anxiety and depression [8]. There are different method to discovering stress ex. Smart watches, chest belt, and extraordinary machine. Our principle objective is to figure out pressure progressively utilizing smart watches through their Sensor. There are different kinds of sensor available to find stress such as PPG, GSR, HRV, ECG and temperature. Smart watches contain a wide range of data through various sensor. This kind of gathered information are applied on various machine learning method. Like linear regression, SVM, KNN, decision tree. Technique have distinct, comparing accuracy and chooses best Machine learning model. This paper investigation have different analysis to find and compare accuracy by various sensors data. It is also check whether using one sensor or multiple sensors such as HRV, ECG or GSR and PPG to predict the better accuracy score for stress detection.

scholarly journals Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Liu ◽  
Yulong Qiao
Keyword(s):  
Machine Learning ◽  
Quasiconformal Mapping ◽  
Kernel Function ◽  
Hyperspectral Image ◽  
Data Classification ◽  
Data Retrieval ◽  
Hyperspectral Data ◽  
The Internet ◽  
Discriminative Ability ◽  
Kernel Machine

Intelligent internet data mining is an important application of AIoT (Artificial Intelligence of Things), and it is necessary to construct large training samples with the data from the internet, including images, videos, and other information. Among them, a hyperspectral database is also necessary for image processing and machine learning. The internet environment provides abundant hyperspectral data resources, but the hyperspectral data have no class labels and no so high value for applications. So, it is important to label the class information for these hyperspectral data through machine learning-based classification. In this paper, we present a quasiconformal mapping kernel machine learning-based intelligent hyperspectral data classification algorithm for internet-based hyperspectral data retrieval. The contributions include three points: the quasiconformal mapping-based multiple kernel learning network framework is proposed for hyperspectral data classification, the Mahalanobis distance kernel function is as the network nodes with the higher discriminative ability than Euclidean distance-based kernel function learning, and the objective function of measuring the class discriminative ability is proposed to seek the optimal parameters of the quasiconformal mapping projection. Experiments show that the proposed scheme is effective for hyperspectral image classification and retrieval.

scholarly journals IoT for Smart Environment Monitoring Based on Python: A Review

2021 ◽  
pp. 57-70
Author(s):  
Saad Hikmat Haji ◽  
Amira B. Sallow
Keyword(s):  
Water Pollution ◽  
Machine Learning ◽  
Internet Of Things ◽  
Environmental Monitoring ◽  
Sensor Technology ◽  
The Internet ◽  
Smart Environment ◽  
Environment Monitoring ◽  
The Internet Of Things

Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.

scholarly journals Rare Variants Detection with Kernel Machine Learning Based on Likelihood Ratio Test

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e93355 ◽  
Author(s):  
Ping Zeng ◽  
Yang Zhao ◽  
Liwei Zhang ◽  
Shuiping Huang ◽  
Feng Chen
Keyword(s):  
Machine Learning ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Rare Variants ◽  
Ratio Test ◽  
Kernel Machine

scholarly journals Advances in Smart Environment Monitoring Systems Using IoT and Sensors

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3113 ◽  
Author(s):  
Silvia Liberata Ullo ◽  
G. R. Sinha
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Sustainable Growth ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Smart Environment ◽  
Environment Monitoring ◽  
Learning Techniques ◽  
Quality Water ◽  
Major Factors

Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

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