Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm

Panwar, Vishwanath; Sharma, Dilip Kumar; Kumar, K.V. Pradeep; Jain, Ankit; Thakar, Chetan M.

doi:10.1016/j.matpr.2021.03.642

Cited by 378 publications

(56 citation statements)

References 13 publications

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“…The main purpose of applying advanced methods for machine learning in companies is to focus on understanding business processes, identifying areas for improvisation, reducing daily work, and thus improving better business processes, product, and service delivery efficient and profitable. For all concerned [ 14 ], the use of basic methods such as deep learning, robotics, automation, and more often has a gradual impact on companies, customers, employees, suppliers, and others [ 15 ]. Using machine learning methods helps management to apply effective methods to collect, process, and organize data from different sources and analyze it in different ways to create models and ideas that help the organisation identify clear opportunities.…”

Section: Discussionmentioning

confidence: 99%

Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)

Devi

Vemuri

Arumugam

et al. 2022

Computational and Mathematical Methods in Medicine

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In the current information and technology era, business enterprises are focusing in performing the process effectively by reducing the waiting time in completing the work, reduce latency and deploy the resources effectively so as to service the patient, medical practitioners, societies, and other stakeholders in an efficient manner. Hence, several organisations are using the emerging technologies so as to obtain high performance and enhance competitive edge. The advancement in machine learning, deep learning, business analytics, etc. enables the health care industry to identify the patterns based on the data collected and create a pivotal position and enhance revenues and profits in a sustainable manner. Machine learning models are considered as computational algorithms which will enable in collected the data, analyze them, and provide the necessary reports to the experts and management in order to make informed decision making. The application of advanced machine learning enables the organisation to process the image effectively, recognize the voice and enable in servicing the customers, process the available data, and identify the patterns so as to make informed decision making. The basic purpose of the study is to analyze the overall implementation of advanced machine learning approaches towards health care services for providing enhanced services, better patient engagement, and support in creating better life for them, the researchers intend to collect the closed-ended questionnaire from employees in different medical centers covering: apprehend the nature of designing and implementation of machine learning approaches in the organisation and understand the effectiveness of these tools in enhancing the sustainable growth and development of the organisation.

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Section: Discussionmentioning

confidence: 99%

Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)

Devi

Vemuri

Arumugam

et al. 2022

Computational and Mathematical Methods in Medicine

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“…e researchers found that critical algorithms such as CART support the predictability of the disease by 93.3%, while traditional models have lower specificity. Deep neural networks can also be used to effectively analyse and detect heart failure, helping physicians make better, more critical clinical decisions [13]. e emergence of artificial intelligence has paved the way for the medical industry to better analyse and detect diseases in the past, enabling physicians and others to provide better care for patients and improve their life expectancy.…”

Section: Discussionmentioning

confidence: 99%

Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis

Bangare

Virmani²,

Karetla

et al. 2022

Journal of Food Quality

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The term heart-related disease is stated as the range of condition that impacts an individual heart negatively. In the current scenario, cardiovascular diseases are causing more deaths when compared with other ailments, it has been estimated that there are nearly 18 million deaths annually as per the recent report released by World Health Organization (WHO). It has been stated that unhealthy habits and other related aspects adopted by individuals are considered as the primary reasons for an increase in the risk of heart diseases. High cholesterol, eating more junk foods, hypertension, etc., created the issue related to heart diseases. Hence, addressing food quality and suggesting better eating habits enable individuals to enhance their living and support better health. The application of new technologies like machine learning, deep learning, and other models support doctors, nurses, and radiologists to predict heart disease effectively. Studies have stated that the various models are used mainly for the classification and forecasting of the diagnosis of heart-related diseases. The researchers have identified that critical algorithms like CART support the predictability of the disease by 93.3% whereas the conventional models possess vert less specificity. Furthermore, deep neural networks can be applied for analyzing and detecting heart failures effectively and supporting medical practitioners in making better and more critical clinical decisions making. The researchers focus on using a descriptive research study for performing the study; moreover, the researcher collates the data using the questionnaire method, which enables sourcing the critical information from the medical practitioners and supports in making critical data analysis effectively. The researchers also use secondary data modes for sourcing the information related to past studies on the related topic. The researchers use the frequency analysis, correlation analysis, and structural equation model analysis for performing the study, and the results are stated in detail in the respective sections.

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“…In terms of diagnosis prediction, the SVM has proven to become the most efficient method (41/ 100 times in investigation). Other image classification algorithms, like CNN, are also 99 percentage effective in the healthcare sector [37].…”

Section: Key Challenges Key Opportunities Blockchain Privacy Concernsmentioning

confidence: 99%

Major Challenges and Future Approaches in the Employment of Blockchain and Machine Learning Techniques in the Health and Medicine

UmaMaheswaran¹,

Prasad

Omarov

et al. 2022

Security and Communication Networks

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According to the benefits in safeguarding and transferring medical information, illness assessment, evaluation of “Magnetic Resonance Mapping” images, and certain other disciplines, blockchain and machine learning (ML) technology has significantly piqued attention in the healthcare domains. Formerly, those chores have been performed out along with individuals; eventually, individuals acquired attraction because to its precision and efficiency. The proposed study will examine the activities and possible capabilities of learning algorithms and blockchain in the healthcare professions focusing on these fascinating facts. Primary and secondary data analysis has been executed, with primary analysis method consisting of a survey of 150 randomly picked medicine professionals with expertise in machine learning and blockchain. They gave their answers that were being subsequently transferred to figures and employed as response variable in SPSS examining. The length of time where learning and blockchain have been used in medicine is really the independent factor. To better understand the primary and small hurdles of integrating machine learning and blockchain, a correlation investigation was done. Thereafter, secondary methodology is employed to validate the primary study results.

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Experimental investigations and optimization of surface roughness in turning of en 36 alloy steel using response surface methodology and genetic algorithm

Cited by 378 publications

References 13 publications

Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)

Design and Implementation of Advanced Machine Learning Management and Its Impact on Better Healthcare Services: A Multiple Regression Analysis Approach (MRAA)

Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis

Major Challenges and Future Approaches in the Employment of Blockchain and Machine Learning Techniques in the Health and Medicine

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