scholarly journals How to Engage Followers: Classifying Fashion Brands According to Their Instagram Profiles, Posts and Comments

2020 ◽  
Author(s):  
Stefanie Scholz ◽  
Christian Winkler
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Topic Models ◽  
Descriptive Statistics ◽  
Machine Learning Techniques ◽  
Maximum Effect ◽  
Mass Market ◽  
Automatic Data ◽  
Learning Techniques ◽  
Automatic Data Analysis

In this article we show how fashion brands communicate with their follower on Instagram. We use a continuously update dataset of 68 brands, more than 300,000 posts and more than 40,000,000 comments. Starting with descriptive statistics, we uncover different behavior and success of the various brands. It turns out that there are patterns specific to luxury, mass-market and sportswear brands. Posting volume is extremely brand dependent as is the number of comments and the engagement of the community. Having understood the statistics, we turn to machine learning techniques to measure the response of the community via comments. Topic models help us understand the structure of their respective community and uncover insights regarding the response to campaigns. Having up-to-date content is essential for this kind of analysis, as the market is highly volatile. Furthermore, automatic data analysis is crucial to measure the success of campaigns and adjust them accordingly for maximum effect.

scholarly journals Energy Meter Data Analysis Using Machine Learning Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 08-13
Author(s):  
Anitha Kumari K ◽  
Indusha M ◽  
Abarna Devi D ◽  
Dheva Dharshini S
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Use Cases ◽  
Machine Learning Techniques ◽  
Energy Usage ◽  
Energy Meter ◽  
Learning Techniques ◽  
Significant Interest ◽  
F Measure

With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavior of the machine and providing insights on usage of the energy. This will help the industries to identify the faults in the machine and to rectify it.Two use cases with two different motor specifications is considered for evaluation and the efficiency is proved by considering accuracy, precision, F-measure and recall as metrics.

Acoustic Diversity Classification Using Machine Learning Techniques: Towards Automated Marine Big Data Analysis

2020 ◽  
Vol 29 (03n04) ◽  
pp. 2060011
Author(s):  
Emna Hachicha Belghith ◽  
François Rioult ◽  
Medjber Bouzidi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Acoustic Diversity ◽  
Marine Data

During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human impact on marine data. Nonetheless, supporting acoustic sounds classification is missing in such environment, with taking into account the diversity of such data (i.e., sounds of living undersea species, sounds of human activities, and sounds of environmental effects). To overcome this issue, we propose in this paper an approach that efficiently allowing acoustic diversity classification using machine learning techniques. The aim is to reach an automated support of marine big data analysis. We have conducted a set of experiments, using a real marine dataset, in order to validate our approach and show its effectiveness and efficiency. To do so, three machine learning techniques are employed: (i) classic machine learning models (i.e., k-nearest neighbor and support vector machine), (ii) deep learning based on convolutional neural networks, and (iii) transfer learning based on the reuse of pretrained models.

Network Support Data Analysis for Fault Identification Using Machine Learning

2019 ◽  
Vol 7 (2) ◽  
pp. 41-49 ◽  
Author(s):  
Shakila Basheer ◽  
Usha Devi Gandhi ◽  
Priyan M.K. ◽  
Parthasarathy P.
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Good Deal ◽  
Machine Learning Techniques ◽  
Fault Identification ◽  
Human Errors ◽  
Network Support ◽  
Learning Techniques ◽  
Technical Solutions ◽  
Operational Data

Machine learning has gained immense popularity in a variety of fields as it has the ability to change the conventional workflow of a process. The abundance of data available serves as the motivation for this. This data can be exploited for a good deal of knowledge. In this article, we focus on operational data of networking devices that are deployed in different locations. This data can be used to predict faults in the devices. Usually, after the deployment of networking devices in customer site, troubleshooting these devices is difficult. Operational data of these devices is needed for this process. Manually analysing the machined produced operational data is tedious and complex due to enormity of data. Using machine learning techniques will be of greater help here as this will help automate the troubleshooting process, avoid human errors and save time for the technical solutions engineers.

Using machine learning techniques in the construction of models. II. Data analysis with rule induction

1997 ◽  
Vol 95 (1) ◽  
pp. 95-111 ◽  
Author(s):  
Sašo Džeroski ◽  
Jasna Grbović ◽  
William J. Walley ◽  
Boris Kompare
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Rule Induction ◽  
Machine Learning Techniques ◽  
Learning Techniques
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