Machine learning classification model using Weibo users' social appearance anxiety

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

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Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

Sustainability ◽

10.3390/su13116376 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6376

Author(s):

Junseo Bae ◽

Sang-Guk Yum ◽

Ji-Myong Kim

Keyword(s):

Machine Learning ◽

Financial Management ◽

Construction Projects ◽

Transportation Infrastructure ◽

Classification Model ◽

Insurance Companies ◽

Economic Damage ◽

Infrastructure Projects ◽

Machine Learning Classification ◽

Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

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Classification Model Simulator: A simulator for different Machine Learning Classification Algorithms

2021 2nd International Conference for Emerging Technology (INCET) ◽

10.1109/incet51464.2021.9456348 ◽

2021 ◽

Author(s):

Abhinandan Singla ◽

Unnati Chaturvedi ◽

Preet Kanwal

Keyword(s):

Machine Learning ◽

Classification Model ◽

Classification Algorithms ◽

Machine Learning Classification

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Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

BJGP Open ◽

10.3399/bjgpopen18x101589 ◽

2018 ◽

Vol 2 (2) ◽

pp. bjgpopen18X101589 ◽

Cited By ~ 5

Author(s):

Emmanuel A Jammeh ◽

Camille, B Carroll ◽

Stephen, W Pearson ◽

Javier Escudero ◽

Athanasios Anastasiou ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Case Control ◽

Classification Model ◽

Dementia Diagnosis ◽

Machine Learning Classification ◽

Primary Care Data ◽

Dementia Risk ◽

Diagnosis Of Dementia ◽

Read Codes

BackgroundUp to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.AimThe aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.Design & settingThe study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.MethodRoutinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.ResultsThe model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.ConclusionThe results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

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Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9100580 ◽

2020 ◽

Vol 9 (10) ◽

pp. 580 ◽

Cited By ~ 1

Author(s):

Maria Antonia Brovelli ◽

Yaru Sun ◽

Vasil Yordanov

Keyword(s):

Machine Learning ◽

Forest Dynamics ◽

Google Earth ◽

Classification Model ◽

Mitigation Measures ◽

Computational Time ◽

Forest Change ◽

Machine Learning Classification ◽

Cloud Processing ◽

Google Earth Engine

Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

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Music Feature Extraction and Classification Algorithm Based on Deep Learning

Scientific Programming ◽

10.1155/2021/1651560 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Jingwen Zhang

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Large Scale ◽

Rapid Development ◽

Audio Signal ◽

Classification Model ◽

Machine Learning Classification ◽

Gating Mechanism ◽

Music Information ◽

Sound Spectrum

With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches.

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