Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 2

2020 ◽  
pp. 2-6
Author(s):  
Sumi Helal ◽  
Flavia C. Delicato ◽  
Cintia B. Margi ◽  
Satyajayant Misra ◽  
Markus Endler
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Challenges And Opportunities

scholarly journals Data Science in Healthcare- Current Challenges and Opportunities

2021 ◽  
Author(s):  
Pankaj Khurana ◽  
Rajeev Varshney
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Data Science ◽  
Healthcare Providers ◽  
Life Sciences ◽  
Machine Learning Algorithms ◽  
Successful Implementation ◽  
Challenges And Opportunities ◽  
Analytical Approaches ◽  
Implementation Factors

The rise in the volume, variety and complexity of data in healthcare has made it as a fertile-bed for Artificial intelligence (AI) and Machine Learning (ML). Several types of AI are already being employed by healthcare providers and life sciences companies. The review summarises a classical machine learning cycle, different machine learning algorithms; different data analytical approaches and successful implementation in haematology. Although there are many instances where AI has been found to be great tool that can augment the clinician’s ability to provide better health outcomes, implementation factors need to be put in place to ascertain large-scale acceptance and popularity.

Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 1

2020 ◽  
pp. 2-8
Author(s):  
Sumi Helal ◽  
Flavia C. Delicato ◽  
Cintia B. Margi ◽  
Satyajayant Misra ◽  
Markus Endler
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Challenges And Opportunities

Revealing Challenges within the Application of Machine Learning Services – A Delphi Study

2021 ◽  
Vol 2 (1) ◽  
pp. 1-29
Author(s):  
Robert Philipp ◽  
Andreas Mladenow ◽  
Christine Strauss ◽  
Alexander Voelz
Keyword(s):  
Machine Learning ◽  
Delphi Study ◽  
Data Science ◽  
Future Research ◽  
It Infrastructure ◽  
New Paradigm ◽  
Research Fields ◽  
Challenges And Opportunities ◽  
Model Training ◽  
Do So

Over the past years, Machine Learning has been applied to an increasing number of problems across numerous industries. However, the steady rise in the application of Machine Learning has not come without challenges since companies often lack the expertise or infrastructure to build their own Machine Learning systems. These challenges led to the emergence of a new paradigm, called Machine Learning as a Service. Scientific literature has mainly analyzed this topic in the context of platform solutions that provide ready-to-use environments for companies. We recently have developed a platform-independent approach and labeled it Machine Learning Services. The aim of the present study is to identify and evaluate challenges and opportunities in the application of Machine Learning Services. To do so, we conducted a Delphi Study with a panel of machine learning experts. The study consisted of three rounds and was structured according to the five steps of the Data Science Lifecycle. A variety of challenges from the areas “Communication”, “Environment”, “Approach”, “Data”, “Retraining, Testing, Monitoring and Updating”, “Model Training and Evaluation” were identified. Subsequently, the challenges revealed by the Delphi Study were compared with previous work on Machine Learning as a Service, which resulted from a structured literature review. The identified areas serve as possible future research fields and give further implications for practice. Alleviating communication issues and assessing the business IT infrastructure prior to the machine learning project are among the key findings of our study.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Data science skills: Building partnership for efficient school curriculum delivery in Africa

2020 ◽  
Vol 36 ◽  
pp. 49-62
Author(s):  
Nureni Olawale Adeboye ◽  
Peter Osuolale Popoola ◽  
Oluwatobi Nurudeen Ogunnusi
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Open Data ◽  
School Curriculum ◽  
Global Perspective ◽  
Special Focus ◽  
African Countries ◽  
Tertiary Institutions ◽  
Data Description ◽  
Curriculum Delivery

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

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