A review of various semi-supervised learning models with a deep learning and memory approach

Bagherzadeh, Jamshid; Asil, Hasan

doi:10.1007/s42044-018-00027-6

Cited by 29 publications

(11 citation statements)

References 23 publications

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“…Labeled data is challenging to access in machine learning, but unlabeled data is frequently collected and accessed quickly. In most initiatives, however, most of the data is unlabeled, but some are [ 80 ]. So, in machine learning and data mining, the primary assumption is that the training and future data have the same distribution and properties [ 81 ].…”

Section: Resultsmentioning

confidence: 99%

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

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Section: Resultsmentioning

confidence: 99%

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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“…Semi-supervised learning approaches include self-training methods [6], generative models [1,64], and graph-and vector-based methods [5]. The most common objective in semi-supervised learning is to directly improve the performance of the supervised learning part [24,39].…”

Section: Related Workmentioning

confidence: 99%

“…Note that a high number of data points is required for the training of the deep models, but a program can be induced from a single demonstration as shown in Section 3. The network included three convolutional layers with 3 kernels each with sizes of (7, 7), (5,5) and (3, 3) with RELU activation. These layers allow the network to extract features from the images.…”

Section: Program Synthesis and Autonomous Controlmentioning

confidence: 99%

Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

Smith

Ramamoorthy

2020

Electron. Proc. Theor. Comput. Sci.

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Semi-supervised learning improves the performance of supervised machine learning by leveraging methods from unsupervised learning to extract information not explicitly available in the labels. Through the design of a system that enables a robot to learn inspection strategies from a human operator, we present a hybrid semi-supervised system capable of learning interpretable and verifiable models from demonstrations. The system induces a controller program by learning from immersive demonstrations using sequential importance sampling. These visual servo controllers are parametrised by proportional gains and are visually verifiable through observation of the robot's position in the environment. Clustering and effective particle size filtering allows the system to discover goals in the state space. These goals are used to label the original demonstration for end-to-end learning of behavioural models. The behavioural models are used for autonomous model predictive control and scrutinised for explanations. We implement causal sensitivity analysis to identify salient objects and generate counterfactual conditional explanations. These features enable decision making interpretation and post hoc discovery of the causes of a failure. The proposed system expands on previous approaches to program synthesis by incorporating repellers in the attribution prior of the sampling process. We successfully learn the hybrid system from an inspection scenario where an unmanned ground vehicle has to inspect, in a specific order, different areas of the environment. The system induces an interpretable computer program of the demonstration that can be synthesised to produce novel inspection behaviours. Importantly, the robot successfully runs the synthesised program on an unseen configuration of the environment while presenting explanations of its autonomous behaviour.

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“…All artificial intelligence models will use some training data such as pictures from neuroimaging techniques and other electronic healthcare data to extract full features or direct samples to classify, detect, and recognize ARD. Numerous ML applications involve tasks that can be set up as supervised and semisupervised learning [15]. ML algorithms often have reached more than 96% to classify AD [16,17].…”

Section: Introductionmentioning

confidence: 99%

Single and Combined Neuroimaging Techniques for Alzheimer’s Disease Detection

Amini

Pedram

Moradi

et al. 2021

Computational Intelligence and Neuroscience

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Alzheimer’s disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

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A review of various semi-supervised learning models with a deep learning and memory approach

Cited by 29 publications

References 23 publications

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study

Single and Combined Neuroimaging Techniques for Alzheimer’s Disease Detection

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