Understanding the link between audience engagement metrics and the perceived quality of online news using machine learning

2021 ◽  
pp. 1-24
Author(s):  
Catherine Sotirakou ◽  
Damian Trilling ◽  
Panagiotis Germanakos ◽  
Dimitrios Alexandros Sinis ◽  
Constantinos Mourlas
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Perceived Quality ◽  
Online News ◽  
Machine Learning Algorithms ◽  
News Story ◽  
Extended Model ◽  
Audience Engagement ◽  
Data Set

This article aims to explain the perceived quality of online news articles. Discovering which elements of a news story influence readers’ perceptions could drive online popularity, which is the paramount factor of digital news readership. This work explores an approach to use tree-based machine learning algorithms to address this problem based on selected characteristics, which measure engagement, drawn from prior research mostly developed by communication scientists. A proposed extended model is used to examine the association between the engagement features and perceived quality concerning all the articles depending mainly on their genre. To demonstrate the capacity of using predictive analytics to facilitate journalistic news writing the proposed methodology is applied on a novel data set with 200K articles in total constructed from a blog site. The results of phase A, indicate interesting correlations between the features and the perceived quality of the articles. In stage B, the paper seeks to extract a set of rules that can be used as guidelines for authors in the writing of their next articles, indicating the probability of popularity that their articles may gain if these rules are taken into consideration.

scholarly journals Protein structure prediction using AI and quantum computers

2021 ◽  
Author(s):  
Ben Geoffrey A S
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Machine Learning Algorithms ◽  
Quantum Computers ◽  
Data Set ◽  
Backbone Atoms ◽  
Protein Contact Maps

This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorithms. The CASP dataset from ProteinNet was downloaded which is a standardized data set for machine learning of protein structure. Its large and standardized dataset of PDB entries contains the coordinates of the backbone atoms, corresponding to the sequential chain of N, C_alpha, and C' atoms. This dataset was used to train a quantum-classical hybrid Keras deep neural network model to predict the structure of the proteins. To visually qualify the quality of the predicted versus the actual protein structure, protein contact maps were generated with the experimental and predicted protein structure data and qualified. Therefore this model is recommended for the use of protein structure prediction using AI leveraging the power of quantum computers. The code is provided in the following Github repository https://github.com/bengeof/Protein-structure-prediction-using-AI-and-quantum-computers.

scholarly journals To Enhance the Quality of HCRS using Fuzzy-Genetic Approach

2022 ◽  
Author(s):  
Latha Banda ◽  
Karan Singh ◽  
Vikash Arya ◽  
Devendra Gautam ◽  
Ali Ahmadian
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Health Care ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Genetic Approach ◽  
Data Set ◽  
Proposed Model ◽  
Fuzzy Genetic

Abstract Social media is recent generation of Recommender Systems (RS). Health Care Recommender System (HCRS) term used to analyse the medical data and then predict the disease of a patient with the help of various techniques used in RS. To ensure the quality and trustworthiness of medical data, machine learning algorithms are applied. Even though, there is a much gap between health care diagnosis and IT solutions. To evade this gap, the hybrid Fuzzy-genetic approach is used in HCRS. In this, Genetic algorithm is used for similarity computations with the help of mutation and crossover operators. Later fuzzy rules are generated for the data set with the additional personalized information of a user. Considering these approaches, the proposed model enhances the quality of recommendation in HCRS.

Detection of Falsely Elevated Point-of-Care Potassium Results Due to Hemolysis Using Predictive Analytics

2020 ◽  
Vol 154 (2) ◽  
pp. 242-247
Author(s):  
Robert C Benirschke ◽  
Thomas J Gniadek
Keyword(s):  
Machine Learning ◽  
Predictive Value ◽  
Predictive Analytics ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Plasma Potassium ◽  
Data Set ◽  
Laboratory Errors ◽  
Rule Out

Abstract Objectives Preanalytical factors, such as hemolysis, affect many components of a test panel. Machine learning can be used to recognize these patterns, alerting clinicians and laboratories to potentially erroneous results. In particular, machine learning might identify which cases of elevated potassium from a point-of-care (POC) basic metabolic panel are likely erroneous. Methods Plasma potassium concentrations were compared between POC and core laboratory basic metabolic panels to identify falsely elevated POC results. A logistic regression model was created using these labels and the other analytes on the POC panel. Results This model has high predictive power in classifying POC potassium as falsely elevated or not (area under the curve of 0.995 when applied to the test data set). A rule-in and rule-out approach further improves the model’s applicability with a positive predictive value of around 90% and a negative predictive value near 100%. Conclusions Machine learning has the potential to detect laboratory errors based on the recognition of patterns in commonly requested multianalyte panels. This could be used to alert providers at the POC that a result is suspicious or used to monitor the quality of POC results.

scholarly journals Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 621
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Network Performance ◽  
Service Providers ◽  
Perceived Quality ◽  
Internet Service Providers ◽  
Performance Parameters ◽  
Internet Service ◽  
Access Points

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

Swarm Intelligence Optimization: An Exploration and Application of Machine Learning Technology

2021 ◽  
Vol 30 (1) ◽  
pp. 460-469
Author(s):  
Yinying Cai ◽  
Amit Sharma
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Research Result ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Data Set ◽  
Rice Pests ◽  
Machine Leaning ◽  
Smart Agriculture ◽  
Swarm Intelligence Optimization

Abstract In the agriculture development and growth, the efficient machinery and equipment plays an important role. Various research studies are involved in the implementation of the research and patents to aid the smart agriculture and authors and reviewers that machine leaning technologies are providing the best support for this growth. To explore machine learning technology and machine learning algorithms, the most of the applications are studied based on the swarm intelligence optimization. An optimized V3CFOA-RF model is built through V3CFOA. The algorithm is tested in the data set collected concerning rice pests, later analyzed and compared in detail with other existing algorithms. The research result shows that the model and algorithm proposed are not only more accurate in recognition and prediction, but also solve the time lagging problem to a degree. The model and algorithm helped realize a higher accuracy in crop pest prediction, which ensures a more stable and higher output of rice. Thus they can be employed as an important decision-making instrument in the agricultural production sector.

scholarly journals Birds Sound Classification Based on Machine Learning Algorithms

2021 ◽  
pp. 1-11
Author(s):  
Aska E. Mehyadin ◽  
Adnan Mohsin Abdulazeez ◽  
Dathar Abas Hasan ◽  
Jwan N. Saeed
Keyword(s):  
Machine Learning ◽  
Noise Suppression ◽  
Bird Species ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Learning Technology ◽  
Species Classification ◽  
Data Set ◽  
Sound Classification ◽  
Mel Frequency Cepstral Coefficient

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

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