An Insight into Machine Learning Algorithms to Map the Occurrence of the Soil Mattic Horizon in the Northeastern Qinghai-Tibetan Plateau

Pedosphere ◽  
2018 ◽  
Vol 28 (5) ◽  
pp. 739-750 ◽  
Author(s):  
Junjun ZHI ◽  
Ganlin ZHANG ◽  
Renmin YANG ◽  
Fei YANG ◽  
Chengwei JIN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tibetan Plateau ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Insight Into

scholarly journals Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China

2022 ◽  
Vol 9 ◽  
Author(s):  
Huilong Lin ◽  
Yuting Zhao
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Tibetan Plateau ◽  
Variable Selection ◽  
Yellow River ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Yellow River ◽  
Optimal Model ◽  
Erosion Loss

The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.

scholarly journals (Not) Hearing Happiness: Predicting Fluctuations in Happy Mood from Acoustic Cues Using Machine Learning

2018 ◽  
Author(s):  
Aaron C. Weidman ◽  
Jessie Sun ◽  
Simine Vazire ◽  
Jordi Quoidbach ◽  
Lyle H Ungar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Analysis ◽  
Emotional State ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Acoustic Features ◽  
Total N ◽  
Happy Mood ◽  
Insight Into

Recent popular claims surrounding virtual assistants suggest that computers will soon be able to hear our emotions. Supporting this possibility, promising work has harnessed big data and emergent technologies to automatically predict stable levels of one specific emotion, happiness, at the community (e.g., counties) and trait (i.e., people) levels. Furthermore, research in affective science has shown that non-verbal vocal bursts (e.g., sighs, gasps) and specific acoustic features (e.g., pitch, energy) can differentiate between distinct emotions (e.g., anger, happiness), and that machine-learning algorithms can detect these differences. Yet, to our knowledge, no work has tested whether computers can automatically detect normal, everyday within-person fluctuations in one emotional state from acoustic analysis. To address this issue in the context of happy mood, across three studies (total N = 20,197), we asked participants to repeatedly report their state happy mood, and to provide audio recordings—including both direct speech and ambient sounds—from which we extracted acoustic features. Using three different machine learning algorithms (neural networks, random forests, and support vector machines) and two sets of acoustic features, we found that acoustic features yielded minimal predictive insight into happy mood above chance. Neither multilevel modeling analyses nor human coders provided additional insight into state happy mood. These findings suggest that it is not yet possible to automatically assess fluctuations in one emotional state (i.e., happy mood) from acoustic analysis, pointing to a critical future direction for affective scientists interested in acoustic analysis of emotion and automated emotion detection.

scholarly journals A Survey of Network Traffic Classification

2020 ◽  
Vol 32 (6) ◽  
pp. 137-154
Author(s):  
Aleksandr Igorevich Getman ◽  
Maria Kirillovna Ikonnikova
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithms ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Traffic Classification ◽  
Research Papers ◽  
Advantages And Disadvantages ◽  
Network Traffic Classification ◽  
Insight Into

This survey is dedicated to the task of network traffic classification, particularly to the use of machine learning algorithms in this task. The survey begins with the description of the task, its variations and possible uses in real-world problems. It then proceeds to the description of the methods used historically to solve this task, their limitations and evolution of traffic making machine learning the main way to solve the problem. Then the most popular machine learning algorithms used in this task are described, with the examples of research papers, providing the insight into their advantages and disadvantages in relation to this field. The task of feature selection is discussed, followed by the more global problem of acquiring the suitable dataset to use in the research; some examples of such popular datasets and their descriptions are provided. The paper concludes with the outline of the current problems in this research area to be solved.

Machine Learning for Big Data

2021 ◽  
pp. 56-76
Author(s):  
Supriya M. S. ◽  
Keerthana Sasidaran
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Full Scale ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Data Process ◽  
Machine Learning Model ◽  
To Come ◽  
Insight Into

Big data and machine learning currently play an important role in various applications and in research. These approaches are explored in depth in this chapter. The chapter starts with a summary of big data and its implementation in a number of fields, and then deals with the problems that big data presents and the need for other technology to resolve these issues/challenges. Big data can best be used with the aid of the machine learning model, even though they are not directly related. Thus, the paradigms of machine learning that support big data can be combined with big data technology, thus providing insight into a range of big data machine learning approaches and techniques. Although big data cannot rely solely on the few paradigms of machine learning, the underlying problems are addressed. New machine learning algorithms are needed that can explore the full scale of the big data process and enable software engineering firms to come up with better solutions.

scholarly journals Creative AI: From Expressive Mimicry to Critical Inquiry

Artnodes ◽  
2020 ◽  
Author(s):  
Angus Forbes
Keyword(s):  
Machine Learning ◽  
New Media ◽  
Feature Detection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Media Arts ◽  
Generative Art ◽  
Applied Machine Learning ◽  
Insight Into

The nascent field of what has come to be known as “creative AI” consists of a range of activities at the intersections of new media arts, human-computer interaction, and artificial intelligence. This article provides an overview of recent projects that emphasise the use of machine learning algorithms as a means to identify, replicate, and modify features in existing media, to facilitate new multimodal mappings between user inputs and media outputs, to push the boundaries of generative art experiences, and to critically investigate the role of feature detection and pattern identification technologies in contemporary life. Despite the proliferation of such projects, recent advances in applied machine learning have not yet been incorporated into or interrogated by creative AI projects, and this article also highlights opportunities for computational artists working in this area. The article concludes by envisioning how creative AI practice could include delineating the boundaries of what can and cannot be learned by extracting features from artefacts and experiences, exploring how new forms of interpretation can be encoded into neural networks, and articulating how the interaction of multiple machine learning algorithms can be used to generate new insight into the intertwining sociotechnical systems that encompass our lives.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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