scholarly journals Technology Management for Accelerated Recovery during COVID-19

2020 ◽  
Vol 3 (5) ◽  
pp. 33-53
Author(s):  
Swapnil Morande ◽  
Veena Tewari
Keyword(s):  
Machine Learning ◽  
Design Research ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Quantitative Methodology ◽  
Time Data ◽  
Treatment Protocols ◽  
Clinical Trial Research ◽  
Current State ◽  
Learning Techniques

Objective- The research looks forward to extracting strategies for accelerated recovery during the ongoing Covid-19 pandemic. Design - Research design considers quantitative methodology and evaluates significant factors from 170 countries to deploy supervised and unsupervised Machine Learning techniques to generate non-trivial predictions. Findings - Findings presented by the research reflect on data-driven observation applicable at the macro level and provide healthcare-oriented insights for governing authorities. Policy Implications - Research provides interpretability of Machine Learning models regarding several aspects of the pandemic that can be leveraged for optimizing treatment protocols. Originality - Research makes use of curated near-time data to identify significant correlations keeping emerging economies at the center stage. Considering the current state of clinical trial research reflects on parallel non-clinical strategies to co-exist with the Coronavirus.

Mining for Creativity: Determining the Creativity of Ideas Through Data Mining Techniques

2017 ◽  
Author(s):  
Christine A. Toh ◽  
Elizabeth M. Starkey ◽  
Conrad S. Tucker ◽  
Scarlett R. Miller
Keyword(s):  
Machine Learning ◽  
Design Research ◽  
Machine Learning Techniques ◽  
Numeric Model ◽  
Design Creativity ◽  
Large Sets ◽  
Learning Techniques ◽  
Ideation Methods ◽  
High Level ◽  
Design Ideas

The emergence of ideation methods that generate large volumes of early-phase ideas has led to a need for reliable and efficient metrics for measuring the creativity of these ideas. However, existing methods of human judgment-based creativity assessments, as well as numeric model-based creativity assessment approaches suffer from low reliability and prohibitive computational burdens on human raters due to the high level of human input needed to calculate creativity scores. In addition, there is a need for an efficient method of computing the creativity of large sets of design ideas typically generated during the design process. This paper focuses on developing and empirically testing a machine learning approach for computing design creativity of large sets of design ideas to increase the efficiency and reliability of creativity evaluation methods in design research. The results of this study show that machine learning techniques can predict creativity of ideas with relatively high accuracy and sensitivity. These findings show that machine learning has the potential to be used for rating the creativity of ideas generated based on their descriptions.

scholarly journals A Survey on Software-Defined Wireless Sensor Networks: Current status, machine learning approaches and major challenges

2021 ◽  
Author(s):  
Fabian Fernando Jurado Lasso ◽  
Letizia Marchegiani ◽  
Jesus Fabian Jurado ◽  
Adnan Abu Mahfouz ◽  
Xenofon Fafoutis
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Management Policy ◽  
Wireless Sensor ◽  
Current Status ◽  
Policy Enforcement ◽  
Learning Approaches ◽  
Current State ◽  
Learning Techniques ◽  
Comprehensive Survey

This paper is aimed to present a comprehensive survey of relevant research over the period 2012-2021 of Software-Defined Wireless Sensor Network (SDWSN) proposals and Machine Learning (ML) techniques to perform network management, policy enforcement, and network configuration functions. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSNs, mainly the current state-of-art, machine learning techniques, and open issues.

Analysis of Crime Report by Data Analytics Using Python

2021 ◽  
pp. 54-79
Author(s):  
G. Maria Jones ◽  
S. Godfrey Winster
Keyword(s):  
Machine Learning ◽  
New Technologies ◽  
Rapid Development ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Complex Data ◽  
Time Data ◽  
Related Data ◽  
Learning Techniques ◽  
And Performance

The ever-rapid development of technology in today's world tends to provide us with a dramatic explosion of data, leading to its accumulation and thus data computation has amplified in comparison to the recent past. To manage such complex data, emerging new technologies are enabled specially to identify crime patterns, as crime-related data is escalating. These digital technologies have the potential to manipulate and also alter the pattern. To combat this, machine learning techniques are introduced which have the ability to analyse such voluminous data. In this work, the authors intend to understand and implement machine learning techniques in real time data analysis by means of Python. The detailed explanation in preparing the dataset, understanding, visualizing the data using pandas, and performance measure of algorithm is evaluated.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

Examining the Evolution of E-Government Development of Nations Through Machine Learning Techniques

2021 ◽  
pp. 85-107
Author(s):  
Niguissie Mengesha ◽  
Anteneh Ayanso
Keyword(s):  
Machine Learning ◽  
Weighted Average ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Member States ◽  
Theoretical Perspectives ◽  
Learning Techniques ◽  
Cluster Profiles ◽  
Over Time ◽  
Government Development

Several initiatives have tried to measure the efforts nations have made towards developing e-government. The UN E-Government Development Index (EGDI) is the only global report that ranks and classifies the UN Member States into four categories based on a weighted average of normalized scores on online service, telecom infrastructure, and human capital. The authors argue that the EGDI fails in showing the efforts of nations over time and in informing nations and policymakers as to what and from whom to draw policy lessons. Using the UN EGDI data from 2008 to 2020, they profile the UN Member States and show the relevance of machine learning techniques in addressing these issues. They examine the resulting cluster profiles in terms of theoretical perspectives in the literature and derive policy insights from the different groupings of nations and their evolution over time. Finally, they discuss the policy implications of the proposed methodology and the insights obtained.

scholarly journals Early Detection of Cardiovascular Disease using Machine learning Techniques an Experimental Study

2020 ◽  
Vol 9 (3) ◽  
pp. 635-641
Keyword(s):  
Machine Learning ◽  
Proper Time ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Techniques ◽  
Entire Body ◽  
Time Data ◽  
Learning Techniques ◽  
Health Related

Human body prioritizes the heart as the second most important organ after the brain. Any disruption in the heart ultimately leads to disruption of the entire body. Being the members of modern era, enormous changes are happening to us on a daily basis that impact our lives in one way or the other. A major disease among top five fatal diseases includes the heart disease which has been consuming lives worldwide. Therefore, the prediction of this disease is of prime importance as it will enable one to take a proper and needful approach at a proper time. Data mining and machine learning are taking out and refining of useful information from a massive amount of data. It is a basic and primary process in defining and discovering useful information and hidden patterns from databases. The flexibility and adaptability of optimization algorithms find its use in dealing with complex non -linear problems. Machine Learning techniques find its use in medical sciences in solving real health-related issues by early prediction and treatment of various diseases. In this paper, six machine learning algorithms are used and then compared accordingly based on the evaluation of performance. Among all classifiers, decision tree outperforms over the other algorithms with a testing accuracy of 97.29%.

scholarly journals CRISPRpred(SEQ): a sequence based tool for sgRNA on target activity prediction [(almost) beating Deep Learning pipelines by traditional machine learning]

2019 ◽  
Author(s):  
Ali Haisam Muhammad Rafid ◽  
Md. Toufikuzzaman ◽  
Mohammad Saifur Rahman ◽  
M. Sohel Rahman
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Genome Editing ◽  
Cell Lines ◽  
Genetic Diseases ◽  
Machine Learning Techniques ◽  
Activity Prediction ◽  
Current State ◽  
Learning Techniques ◽  
Target Activity

AbstractAn accurate and fast genome editing tool can be used to treat genetic diseases, modify crops genetically etc. However, a tool that has low accuracy can be risky to use, as incorrect genome editing may have severe consequences. Although many tools have been developed in the past, there are still room for further improvement. In this paper, we present CRISPRpred(SEQ), a sequence based tool for sgRNA on target activity prediction that leverages only traditional machine learning techniques. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. In spite of using only traditional machine learning methods, we are able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines), which is quite outstanding.

scholarly journals Use of Machine Learning in Stock Market Prediction

2020 ◽  
Vol 4 (1) ◽  
pp. 60-73
Author(s):  
Memoona Shaheen ◽  
Mehreen Arshad
Keyword(s):  
Machine Learning ◽  
Policy Implications ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Hybrid Techniques ◽  
Current Trends ◽  
Time Period ◽  
Learning Techniques ◽  
Vector Machines ◽  
Prediction Problems

Objective: The objective of this study was to examine and determine future directions in regard to future machine learning techniques based on the review of the current literature. Methodology: A systematic review has been used to review the current trends from the peer-reviewed journal articles in the past twenty years. For this study, four categories have been categorized, the use of neural networks, support vector machines, the use of a genetic algorithm, and the combination of hybrid techniques. Studies in each of these categorize have been evaluated. Finding: Firstly, there is a strong link between machine learning methods and the prediction problems they are associated with. The second conclusion that we can conclude from this review is that past studies need to improve its generalizability results. Most of the studies that have been reviewed in this analysis has only used the machine learning systems through the use of one market or during only a one time period without taking into consideration whether the system would be adaptable in other situations and conditions. Limitations, future trends, as well as policy implications have been defined.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2019 ◽  
pp. 1-38
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Industrial growth in sub-Saharan Africa: evidence from machine learning with insights from nightlight satellite images

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Christian Otchia ◽  
Simplice Asongu
Keyword(s):  
Machine Learning ◽  
Natural Resources ◽  
Industrial Development ◽  
Satellite Images ◽  
Sub Saharan Africa ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Industrial Growth ◽  
Content Type ◽  
Learning Techniques

PurposeThis study uses machine machine learning techniques to assess industrial development in Africa.Design/methodology/approachThis study uses nightlight time data and machine learning techniques to assess industrial development in Africa.FindingsThis study provides evidence on how machine learning techniques and nightlight data can be used to assess economic development in places where subnational data are missing or not precise. Taken together, the research confirms four groups of important determinants of industrial growth: natural resources, agriculture growth, institutions and manufacturing imports. Our findings indicate that Africa should follow a more multisector approach for development, putting natural resources and agriculture productivity growth at the forefront.Originality/valueStudies on the use of machine learning (with insights from nightlight satellite images) to assess industrial development in Africa are sparse.

Sign in / Sign up

Export Citation Format

Share Document