Correction to: Feature extraction and machine learning solutions for detecting motion vector data embedding in HEVC videos

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v5i3.1066 ◽

2020 ◽

pp. 235-242

Author(s):

Farrikh Alzami ◽

Erika Devi Udayanti ◽

Dwi Puji Prabowo ◽

Rama Aria Megantara

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Random Forest ◽

Sentiment Analysis ◽

Classification Performance ◽

Document Preparation ◽

Learning Models ◽

Polarity Classification ◽

Negative Sentiment ◽

Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

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A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments

Sensors ◽

10.3390/s21041274 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1274

Author(s):

Daniel Bonet-Solà ◽

Rosa Ma Alsina-Pagès

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Best Practice ◽

Nearest Neighbor ◽

Gaussian Mixture ◽

Machine Learning Algorithms ◽

Multimedia Retrieval ◽

Natural Environments ◽

K Nearest Neighbor ◽

Acoustic Environments

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.

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Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning

Archives of Suicide Research ◽

10.1080/13811118.2021.1955783 ◽

2021 ◽

pp. 1-16

Author(s):

Woojin Jung ◽

Donghun Kim ◽

Seojin Nam ◽

Yongjun Zhu

Keyword(s):

Machine Learning ◽

Social Media ◽

Feature Extraction ◽

Text Feature

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Multi-Sensor Fusion Module for Perceptual Target Recognition for Intelligent Machine Learning Visual Feature Extraction

IEEE Sensors Journal ◽

10.1109/jsen.2021.3061207 ◽

2021 ◽

pp. 1-1

Author(s):

Hechuang Wang

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Sensor Fusion ◽

Target Recognition ◽

Visual Feature ◽

Intelligent Machine ◽

Visual Feature Extraction

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Utilising Flow Aggregation to Classify Benign Imitating Attacks

Sensors ◽

10.3390/s21051761 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1761

Author(s):

Hanan Hindy ◽

Robert Atkinson ◽

Christos Tachtatzis ◽

Ethan Bayne ◽

Miroslav Bures ◽

...

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Network Traffic ◽

Cyber Attacks ◽

Detection Accuracy ◽

Computational Power ◽

Human Understanding ◽

Flow Aggregation ◽

Additional Level ◽

Attack Surfaces

Cyber-attacks continue to grow, both in terms of volume and sophistication. This is aided by an increase in available computational power, expanding attack surfaces, and advancements in the human understanding of how to make attacks undetectable. Unsurprisingly, machine learning is utilised to defend against these attacks. In many applications, the choice of features is more important than the choice of model. A range of studies have, with varying degrees of success, attempted to discriminate between benign traffic and well-known cyber-attacks. The features used in these studies are broadly similar and have demonstrated their effectiveness in situations where cyber-attacks do not imitate benign behaviour. To overcome this barrier, in this manuscript, we introduce new features based on a higher level of abstraction of network traffic. Specifically, we perform flow aggregation by grouping flows with similarities. This additional level of feature abstraction benefits from cumulative information, thus qualifying the models to classify cyber-attacks that mimic benign traffic. The performance of the new features is evaluated using the benchmark CICIDS2017 dataset, and the results demonstrate their validity and effectiveness. This novel proposal will improve the detection accuracy of cyber-attacks and also build towards a new direction of feature extraction for complex ones.

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