scholarly journals Fire Safety in Indian Coal Mines using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 4003-4005
Keyword(s):  
Machine Learning ◽  
Coal Mining ◽  
Coal Mines ◽  
Mining Industry ◽  
Mining Operation ◽  
Machine Learning Techniques ◽  
Fire Hazards ◽  
Indian Coal ◽  
Learning Software ◽  
The Right

There are around 493 coal mines in India (300+ underground and around190 opencast mines) engaged in coal production for meeting energy and other requirements of our country. Coal and the process of mining itself creates an environment conducive for self-oxidation leading to build up of heat and subsequently break out of fire. This causes safety hazards, decrease in production, increased in de-settlement of colonies, fire related fatalities and risk to life and property. Occurrence of fires in coal mines has always been an undesirable proposition for the coal mining community worldwide due to its high hazard potential towards loss of human lives and property. However, with advent of AI/ML and deep learning, there emerges a vast scope of leveraging its application towards significantly reducing fire hazards in coal mining. Data capturing from such fiery mines, providing machine learning and predicting it beforehand for similar mining situations would significantly enhance safety standard in coal mining industry. This project proposes to develop an algorithm on getting input data from the past incidences/accidents of fire in coal mines and apply machine learning software to help it learn pattern/features vis a vis the fire outcomes. Once the learning is over and data trained, the programme would process the test data of other active projects and may predict for fire threat during forthcoming mining operation. The algorithm aims to enable mining personnel to assess and evaluate the risk of fire in their workplace and take informed decisions based on the predictions based on Machine learning outputs. Also, active fires can as well be studied and predicted in a similar way. This will help the mining team to decide about the right approach of continuing mining operation in such an affected area.

A study on key lean enablers of the coal mining sector using ISM, MICMAC and SEM

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sorokhaibam Khaba ◽  
Chandan Bhar ◽  
Ankita Ray
Keyword(s):  
Conceptual Model ◽  
Coal Mining ◽  
Questionnaire Survey ◽  
Coal Mines ◽  
Mining Industry ◽  
Mining Sector ◽  
Lean Implementation ◽  
Content Type ◽  
Coal Mining Industry ◽  
Indian Coal

PurposeThe purpose of this research is to identify and study the contextual relationships of the significant lean enablers in the Indian coal mining industry using the application of interpretive structural modeling (ISM), matrice d' impacts croisés-multiplication appliquée á un classement (MICMAC) and structural equation modeling (SEM).Design/methodology/approachIn this study, a conceptual model based on ISM was developed forming a hierarchy and contextual relationships of significant enablers for lean implementation in the Indian coal mining industry using a literature review and eliciting expert opinion, which is followed by MICMAC for grouping of enablers and questionnaire survey to validate the ISM based conceptual model using SEM.FindingsThe study modeled and analyzed ten significant enablers of lean implementation in the Indian coal mining industry. The findings suggest that the most important lean enablers in the Indian coal mining industry are employee empowerment, employee motivation and commitment, consistent financial performance measurement and management support.Research limitations/implicationsJudgmental sampling was used for selecting the respondents for conducting the questionnaire survey in this research work as there are a few numbers of coal mines implementing lean principles in India. Although the study was not restricted to a particular part of India with the sample representing the heterogeneous population, the study represents more data from the coal mines in eastern India.Practical implicationsThe model on lean enablers would help the researchers, decision-makers and practitioners to anticipate potential lean enablers in the Indian coal mines and rank the enablers for improved and efficient usage of the available resources creating value to customers with lean and to sustenance academic research on lean.Originality/valueStudies on lean enablers in the mining sector are scarce in the literature, and this study is a novel contribution of exploring lean enablers in the Indian coal mining industry using an integrated approach of ISM–MICMAC and SEM.

scholarly journals Deep Regressor: Cross Subject Academic Performance Prediction System for University Level Students

2019 ◽  
Vol 8 (11S) ◽  
pp. 1265-1267
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Evaluation Process ◽  
Education Institution ◽  
Drop Out ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Current Program ◽  
The Right ◽  
The University

Predicting the academic performance of students has been an important research topic in the Educational field. The main aim of a higher education institution is to provide quality education for students. One way to accomplish a higher level of quality of education is by predicting student’s academic performance and there by taking earlyre- medial actions to improve the same. This paper presents a system which utilizes machine learning techniques to classify and predict the academic performance of the students at the right time before the drop out occurs. The system first accepts the performance parameters of the basic level courses which the student had already passed as these parameters also influence the further study. To pre- dict the performance of the current program, the system continuously accepts the academic performance parame- ters after each academic evaluation process. The system employs machine learning techniques to study the aca- demic performance of the students after each evaluation process. The system also learns the basic rules followed by the University for assessing the students. Based on the present performance of the students, the system classifies the students into different levels and identify the students at high risk. Earlier prediction can help the students to adopt suitable measures in advance to improve the per for- man ce. The systems can also identify the factor saffecting the performance of the same students which helps them to take remedial measures in advance.

scholarly journals Application of seismic multiattribute machine learning to determine coal strata thickness

2021 ◽  
Vol 18 (6) ◽  
pp. 834-844
Author(s):  
Yanhui Wu ◽  
Wei Wang ◽  
Guowei Zhu ◽  
Peng Wang
Keyword(s):  
Machine Learning ◽  
Coal Mining ◽  
Accurate Determination ◽  
Mining Industry ◽  
Seismic Exploration ◽  
Practical Significance ◽  
Application Performance ◽  
Working Face ◽  
Geological Conditions ◽  
Single Attribute

Abstract The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

scholarly journals Reduction of Dust in the Longwall Faces of Coal Mines: Problems and Perspective Solutions

2021 ◽  
pp. 84-97
Keyword(s):  
Coal Mining ◽  
Renewable Energy Sources ◽  
Coal Mines ◽  
Mining Industry ◽  
Dust Concentration ◽  
Dust Particles ◽  
Coal Production ◽  
Mining Operations ◽  
Dust Composition ◽  
High Dust

Despite the increasing reliance on alternative and renewable energy sources in recent years, coal is set to continue being the most vital element of the global energy sector. The world coal supply (1,070 billion tons) shall last for 130 years with the current mining levels. In contrast to some large countries (such as the USA and Germany) reducing their coal production and consumption, Russia plans to increase the coal production levels as part of its strategy regarding the future of the coal mining industry. The annual volume of coal output is more than 440 million tons, 1/3 of which is extracted underground. The current and projected levels of underground coal mining present a set of issues pertaining to elevated dust concentration in the air and increased dust dispersion. High dust concentration in the air leads to damage to the skin, mucous membranes and respiratory organs of workers. Also, with high dust content, visibility in the longwalls decreases, the risk of injury and accidents increases. The present article deals with the formation of detrimental dust conditions that happen in the course of cleaning and preparatory mining operations in coal mines. The article reviews the international practices on dust reduction in coal mining operations and provides an overview of studies on dustiness levels and airborne dust composition in longwall faces of coal mines. It also presents mathematical models dealing with projections on dust composition, including projections on most hazardous dust particles the size of 0.1-10 and 0.1-35 μm. The article also presents a newly developed wetting method showing increased effectiveness.

A Survey on Precision Treatment for Humans Using Cognitive Machine Learning Techniques

2020 ◽  
pp. 79-106
Author(s):  
M. Srivani ◽  
T. Mala ◽  
Abirami Murugappan
Keyword(s):  
Machine Learning ◽  
Systems Approach ◽  
Disease Risk ◽  
Case Analysis ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Current Status ◽  
The People ◽  
Treatment Gains ◽  
The Right

Personalized treatment (PT) is an emerging area in healthcare that provides personalized health. Personalized, targeted, or customized treatment gains more attention by providing the right treatment to the right person at the right time. Traditional treatment follows a whole systems approach, whereas PT unyokes the people into groups and helps them in rendering proper treatment based on disease risk. In PT, case by case analysis identifies the current status of each patient and performs detailed investigation of their health along with symptoms, signs, and difficulties. Case by case analysis also aids in constructing the clinical knowledge base according to the patient's needs. Thus, PT is a preventive medicine system enabling optimal therapy and cost-effective treatment. This chapter aims to explore how PT is served in works of literature by fusing machine learning (ML) and artificial intelligence (AI) techniques, which creates cognitive machine learning (CML). This chapter also explores the issues, challenges of traditional medicine, applications, models, pros, and cons of PT.

Prediction and Recommendation System for Diabetes Using Machine Learning Models

2022 ◽  
pp. 316-327
Author(s):  
Nareshkumar Mustary ◽  
Phani Kumar Singamsetty
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Medical Center ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diabetes Prediction ◽  
Artery Disease ◽  
The Right ◽  
Deep Learning Model ◽  
Machine Learning Models

Diabetes is one of the most deadly diseases on the planet. It is also a cause of a variety of illnesses, such as coronary artery disease, blindness, and urinary organ disease. In this situation, the patient must visit a medical center to obtain their results following consultation. Finding the right combination of characteristics and machine learning techniques for classification is also very critical. However, with the advancement of machine learning techniques, we now have the potential to find a solution to the current problem. The healthcare recommendation system (HRS) may be designed to predict health by evaluating patient lifestyle, physical health, mental health aspects using machine learning. For example, training the model using people's age and diabetes helps to predict new patients without a specific diagnostic for diabetes. The proposed deep learning model with convolutional neural network (D-CNN) achieves an overall accuracy of 96.25%. D-CNN is found to be more successful for diabetes prediction than other machine learning (ML) approaches in the experimental analysis.

Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Omar Farooq ◽  
Parminder Singh
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Classification ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Resource Constrained ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Right ◽  
Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Sign in / Sign up

Export Citation Format

Share Document