scholarly journals Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings

2021 ◽  
Author(s):  
Łukasz Wojtecki ◽  
Sebastian Iwaszenko ◽  
Derek B. Apel ◽  
Mirosława Bukowska ◽  
Janusz Makówka
Keyword(s):  
Machine Learning ◽  
Coal Mine ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Rockburst Hazard ◽  
Underground Coal Mine ◽  
Mine Openings

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

scholarly journals PigLeg: prediction of swine phenotype using machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8764 ◽  
Author(s):  
Siroj Bakoev ◽  
Lyubov Getmantseva ◽  
Maria Kolosova ◽  
Olga Kostyunina ◽  
Duane R. Chartier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithms ◽  
Average Daily Gain ◽  
Nearest Neighbors ◽  
The State ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Leg Weakness

Industrial pig farming is associated with negative technological pressure on the bodies of pigs. Leg weakness and lameness are the sources of significant economic loss in raising pigs. Therefore, it is important to identify the predictors of limb condition. This work presents assessments of the state of limbs using indicators of growth and meat characteristics of pigs based on machine learning algorithms. We have evaluated and compared the accuracy of prediction for nine ML classification algorithms (Random Forest, K-Nearest Neighbors, Artificial Neural Networks, C50Tree, Support Vector Machines, Naive Bayes, Generalized Linear Models, Boost, and Linear Discriminant Analysis) and have identified the Random Forest and K-Nearest Neighbors as the best-performing algorithms for predicting pig leg weakness using a small set of simple measurements that can be taken at an early stage of animal development. Measurements of Muscle Thickness, Back Fat amount, and Average Daily Gain were found to be significant predictors of the conformation of pig limbs. Our work demonstrates the utility and relative ease of using machine learning algorithms to assess the state of limbs in pigs based on growth rate and meat characteristics.

A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine

2020 ◽  
Vol 68 (2) ◽  
pp. 325-336 ◽  
Author(s):  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Quang-Hieu Tran ◽  
Pham Van Hoa ◽  
Dinh-An Nguyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Coal Mine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Open Pit ◽  
Ensemble Machine Learning

scholarly journals Development of Classification Methods for Wheeze and Crackle Using Mel Frequency Cepstral Coefficient (MFCC): A Deep Learning Approach

2020 ◽  
Vol 6 (2) ◽  
pp. 107-114
Author(s):  
Tinir Mohamed Sadi ◽  
Raini Hassan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Common Method ◽  
Learning Approach ◽  
Classification Methods ◽  
Mel Frequency Cepstral Coefficient

The most common method used by physicians and pulmonologists to evaluate the state of the lung is by listening to the acoustics of the patient's breathing by a stethoscope. Misdiagnosis and eventually, mistreatment are rampant if auscultation is not done properly. There have been efforts to address this problem using a myriad of machine learning algorithms, but little has been done using deep learning. A CNN model with MFCC is expected to mitigate these problems. The problem has been in the paucity of large enough datasets. Results show 0.76 and 0.60 for recall for wheeze and crackle respectively, these number are set to increase with optimization.

scholarly journals Hasta la vista, baby: reflections on the risks of algocracy, killer robots, and artificial superintelligence

2021 ◽  
Vol 70 (279-1) ◽  
pp. 45
Author(s):  
Pedro Rubim Borges Fortes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Technological System ◽  
Checks And Balances ◽  
Autonomous Weapons ◽  
Design Requirements ◽  
Rules Of The Game

<p>The neologism Algocracy may mean government or governance by algorithms. Architects of artificial intelligence have perspectives on killer robots and government by artificial superintelligence and are engaged in public debate on both themes. The risks of being dominated by artificial superintelligence and of being subjected to undemocratic, unconstitutional or illegal algo norms inspires our reflection. Institutions should organize rules of the game that prevent machine learning algorithms from learning how to dominate humans. Algorithms need new design requirements to incorporate responsibility, transparency, auditability, incorruptibility, and predictability. The algorithmic responsibility of the state, national public policies for developing a trustworthy AI, and the algorithmic law of killer robots and artificial superintelligence could reduce the risks of algocracy. The particular character of algorithms demands a special discipline to control their power, architecture, and commands. law and government can channel the development and use of killer robots, eventually even setting a global prohibition of autonomous weapons. Likewise, the threat of government by algorithms posed by the emergence of an artificial superintelligence that dominates humankind also requires the development of a new algorithmic law that establishes checks and balances and controls the technological system.<em></em></p>

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.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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