Big data and machine learning with hyperspectral information in agriculture
KLM Ang, JKP Seng - IEEE Access, 2021 - ieeexplore.ieee.org
Hyperspectral and multispectral information processing systems and technologies have
demonstrated its usefulness for the improvement of agricultural productivity and practices by …
demonstrated its usefulness for the improvement of agricultural productivity and practices by …
[HTML][HTML] A survey of malware detection using deep learning
The problem of malicious software (malware) detection and classification is a complex task,
and there is no perfect approach. There is still a lot of work to be done. Unlike most other …
and there is no perfect approach. There is still a lot of work to be done. Unlike most other …
Robust sparse linear discriminant analysis
Linear discriminant analysis (LDA) is a very popular supervised feature extraction method
and has been extended to different variants. However, classical LDA has the following …
and has been extended to different variants. However, classical LDA has the following …
Machine learning-based colorimetric determination of glucose in artificial saliva with different reagents using a smartphone coupled μPAD
Abstract Potassium iodide (KI) and 3, 3′, 5, 5′-tetramethylbenzidine (TMB) are frequently
used as chromogenic agents in μ PADs for glucose determination. Chitosan (Chi) has …
used as chromogenic agents in μ PADs for glucose determination. Chitosan (Chi) has …
Joint sparse principal component analysis
Principal component analysis (PCA) is widely used in dimensionality reduction. A lot of
variants of PCA have been proposed to improve the robustness of the algorithm. However …
variants of PCA have been proposed to improve the robustness of the algorithm. However …
A big data-as-a-service framework: State-of-the-art and perspectives
Due to the rapid advances of information technologies, Big Data, recognized with 4Vs
characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as …
characteristics (volume, variety, veracity, and velocity), bring significant benefits as well as …
[PDF][PDF] Locality adaptive discriminant analysis.
Abstract Linear Discriminant Analysis (LDA) is a popular technique for supervised
dimensionality reduction, and its performance is satisfying when dealing with Gaussian …
dimensionality reduction, and its performance is satisfying when dealing with Gaussian …
Compound Rank- Projections for Bilinear Analysis
In many real-world applications, data are represented by matrices or high-order tensors.
Despite the promising performance, the existing 2-D discriminant analysis algorithms …
Despite the promising performance, the existing 2-D discriminant analysis algorithms …
Sparse representation classifier steered discriminative projection with applications to face recognition
A sparse representation-based classifier (SRC) is developed and shows great potential for
real-world face recognition. This paper presents a dimensionality reduction method that fits …
real-world face recognition. This paper presents a dimensionality reduction method that fits …
Using the original and 'symmetrical face'training samples to perform representation based two-step face recognition
A limited number of available training samples have become one bottleneck of face
recognition. In real-world applications, the face image might have various changes owing to …
recognition. In real-world applications, the face image might have various changes owing to …