Modeling And Optimizing of Different Quality Characteristics In Electrical Discharge Drilling Of Titanium Alloy (Grade-5) Sheet

Jain, Ankit; Pandey, Arun Kumar

doi:10.1016/j.matpr.2019.06.292

Cited by 307 publications

(34 citation statements)

References 17 publications

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“…e researchers have focused on using the descriptive research design as the area of application of machine learning in financial decision making is gaining importance in the emerging economies. Moreover, business leaders are looking to implement new technologies so that they can effectively manage the risk, optimise the cash inflows and outflows, support in better pricing of the securities, and apprehend the areas to generate better financial returns [15].…”

Section: Methodsmentioning

confidence: 99%

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Sujith

Khan

Dornadula³

et al. 2022

Journal of Food Quality

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Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making .

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Section: Methodsmentioning

confidence: 99%

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Sujith

Khan

Dornadula³

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…Companies now have the opportunity to collect more digital data, gain the insights they need, and revolutionize business and thinking. As a result, we are likely to see much-needed market development: faster markets, simpler rules and processes, dynamic transactions and transactions, higher returns, well-informed and informed customers, and motivated transactions [ 10 ]. Although the invention of most artificial intelligence creates economic development by only recognizing innovative openings and interruptions and is pleasing to the eye in modern action, a market segment that has been ignored by other business moguls or enters new markets provides connected devices [ 11 ].…”

Section: Review Of Literaturementioning

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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“…For investigating more accurately various neurological as well as psychiatric disorders worldwide, ML approaches can be greatly used. It can also be used for predicting and analysing numerous health-related outcomes [ 2 ]. However, by using the SEM model, this analysis can be easily conducted by manipulating different predictions of functions.…”

Section: Introductionmentioning

confidence: 99%

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM)

Baker¹,

Padmaja

Puviarasi

et al. 2022

Computational and Mathematical Methods in Medicine

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Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists’ critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.

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Modeling And Optimizing of Different Quality Characteristics In Electrical Discharge Drilling Of Titanium Alloy (Grade-5) Sheet

Cited by 307 publications

References 17 publications

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

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

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM)

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