760 research outputs found
Utilizing Analytical Hierarchy Process for Pauper House Programme in Malaysia
In Malaysia, the selection and evaluation of candidates for
Pauper House Programme (PHP) are done manually. In
this paper, a technique based on Analytical Hierarchy
Technique (AHP) is designed and developed in order to
make an evaluation and selection of PHP application. The
aim is to ensure the selection process is more precise,
accurate and can avoid any biasness issue. This technique
is studied and designed based on the Pauper assessment
technique from one of district offices in Malaysia. A
hierarchical indexes are designed based on the criteria that
been used in the official form of PHP application. A
number of 23 samples of data which had been endorsed
by Exco of State in Malaysia are used to test this
technique. Furthermore the comparison of those two
methods are given in this paper. All the calculations of
this technique are done in a software namely Expert
Choice version 11.5. By comparing the manual and AHP
shows that there are three (3) samples that are not
qualified. The developed technique also satisfies in term
of ease of accuracy and preciseness but need a further
study due to some limitation as explained in the
recommendation of this paper
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of these data. Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain. This is fundamental in the driving sector of Remote Sensing where hyperspectral technology was born and has mostly developed, but it is perhaps even more true in the multitude of current and evolving application sectors that involve these imaging technologies. The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, we want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields other than Remote Sensing are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends
Apple Flower Detection Using Deep Convolutional Networks
To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability
IDENAS: Internal Dependency Exploration for Neural Architecture Search
Machine learning is a powerful tool for extracting valuable information and
making various predictions from diverse datasets. Traditional algorithms rely
on well-defined input and output variables however, there are scenarios where
the distinction between the input and output variables and the underlying,
associated (input and output) layers of the model, are unknown. Neural
Architecture Search (NAS) and Feature Selection have emerged as promising
solutions in such scenarios. This research proposes IDENAS, an Internal
Dependency-based Exploration for Neural Architecture Search, integrating NAS
with feature selection. The methodology explores internal dependencies in the
complete parameter space for classification involving 1D sensor and 2D image
data as well. IDENAS employs a modified encoder-decoder model and the
Sequential Forward Search (SFS) algorithm, combining input-output configuration
search with embedded feature selection. Experimental results demonstrate
IDENASs superior performance in comparison to other algorithms, showcasing its
effectiveness in model development pipelines and automated machine learning. On
average, IDENAS achieved significant modelling improvements, underscoring its
significant contribution to advancing the state-of-the-art in neural
architecture search and feature selection integration.Comment: 57 pages, 19 figures + appendix, the related software code can be
found under the link: https://github.com/viharoszsolt/IDENA
Unsupervised Representation Learning for Diverse Deformable Shape Collections
We introduce a novel learning-based method for encoding and manipulating 3D
surface meshes. Our method is specifically designed to create an interpretable
embedding space for deformable shape collections. Unlike previous 3D mesh
autoencoders that require meshes to be in a 1-to-1 correspondence, our approach
is trained on diverse meshes in an unsupervised manner. Central to our method
is a spectral pooling technique that establishes a universal latent space,
breaking free from traditional constraints of mesh connectivity and shape
categories. The entire process consists of two stages. In the first stage, we
employ the functional map paradigm to extract point-to-point (p2p) maps between
a collection of shapes in an unsupervised manner. These p2p maps are then
utilized to construct a common latent space, which ensures straightforward
interpretation and independence from mesh connectivity and shape category.
Through extensive experiments, we demonstrate that our method achieves
excellent reconstructions and produces more realistic and smoother
interpolations than baseline approaches.Comment: Accepted at International Conference on 3D Vision 202
Applied Machine Learning for Classification of Musculoskeletal Inference using Neural Networks and Component Analysis
Artificial Intelligence (AI) is acquiring more recognition than ever by researchers and machine learning practitioners. AI has found significance in many applications like biomedical research for cancer diagnosis using image analysis, pharmaceutical research, and, diagnosis and prognosis of diseases based on knowledge about patients\u27 previous conditions. Due to the increased computational power of modern computers implementing AI, there has been an increase in the feasibility of performing more complex research.
Within the field of orthopedic biomechanics, this research considers complex time-series dataset of the sit-to-stand motion of 48 Total Hip Arthroplasty (THA) patients that was collected by the Human Dynamics Laboratory at the University of Denver. The research focuses on predicting the motion quality of the THA patients by analyzing the loads acting on muscles and joints during one motion cycle. We have classified the motion quality into two classes: Fair and Poor , based on muscle forces, and have predicted the motion quality using joint angles.
We address different types of Machine Learning techniques: Artificial Neural Networks (LSTM - long short-term memory, CNN - convolutional neural network, and merged CNN-LSTM) and data science approach (principal component analysis and parallel factor analysis), that utilize remodeled datasets: heatmaps and 3-dimensional vectors. These techniques have been demonstrated efficient for the classification and prediction of the motion quality.
The research proposes time-based optimization by predicting the motion quality at an initial stage of musculoskeletal model simulation, thereby, saving time and efforts required to perform multiple model simulations to generate a complete musculoskeletal modeling dataset. The research has provided efficient techniques for modeling neural networks and predicting post-operative musculoskeletal inference. We observed the accuracy of 83.33% for the prediction of the motion quality under the merged LSTM and CNN network, and autoencoder followed by feedforward neural network. The research work not only helps in realizing AI as an important tool for biomedical research but also introduces various techniques that can be utilized and incorporated by engineers and AI practitioners while working on a multi-variate time-series wide shaped data set with high variance
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