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Advanced Machine Learning Techniques, Applications and Developments
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Advanced Machine Learning Techniques, Applications and Developments

A special issue of Applied System Innovation (ISSN 2571-5577).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 26235

Special Issue Editors

Dr. Patrícia Ramos

E-Mail Website
Guest Editor
1. CEOS.PP, ISCAP, Polytechnic of Porto, 4465-004 Porto, Portugal
2. INESC TEC, 4200-465 Porto, Portugal
Interests: statistical modelling; forecasting; optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jose Manuel Oliveira

E-Mail Website
Guest Editor
1. Faculty of Economics, University of Porto, 4200-464 Porto, Portugal
2. INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: time series forecasting; machine learning; deep learning; data science; big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industries are heavily investing in new digital technologies such as AI, machine learning, and big data analysis, aiming for higher efficiency and new business opportunities. AI is expected to radically change the way companies operate and generate value. For example, in the manufacturing industry, AI creates many opportunities for companies, such as preventive and predictive maintenance, identification of defects in the manufacturing process, demand and forecasting tools, and inventory planning. They hold promise as drivers of some of the most influential research in the twenty-first century since they generate innovation in a wide range of research fields, encompassing such diverse areas as healthcare, logistics and distribution, manufacturing industries, business technology, transportation, energy, environmental issues, etc. The disciplines are inherently multidisciplinary, involving mathematics, physics, and computing.

Against this backdrop, this Special Issue calls for a more critical discussion and outlook for the real-world applications in AI and machine learning and the latest progress in utilizing these groundbreaking technologies, and aims to share gained insights. We invite authors to contribute original research articles addressing significant issues and contributing towards the development of new concepts, methodologies, applications, trends, and knowledge in science. Review articles describing the current state of the art are also welcome.

Prof. Dr. Patrícia Ramos
Prof. Dr. José Oliveira
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied System Innovation is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (4 papers)

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Research

16 pages, 2041 KiB  
Article
A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM
by Barakat AlBadani, Ronghua Shi and Jian Dong
Appl. Syst. Innov. 2022, 5(1), 13; https://doi.org/10.3390/asi5010013 - 14 Jan 2022
Cited by 83 | Viewed by 9311
Abstract
Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the [...] Read more.
Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques, Applications and Developments)
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20 pages, 1521 KiB  
Article
A Comparative Analysis of Active Learning for Biomedical Text Mining
by Usman Naseem, Matloob Khushi, Shah Khalid Khan, Kamran Shaukat and Mohammad Ali Moni
Appl. Syst. Innov. 2021, 4(1), 23; https://doi.org/10.3390/asi4010023 - 15 Mar 2021
Cited by 48 | Viewed by 6077
Abstract
An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could [...] Read more.
An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques, Applications and Developments)
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17 pages, 3272 KiB  
Article
Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
by Jaideep Singh and Matloob Khushi
Appl. Syst. Innov. 2021, 4(1), 17; https://doi.org/10.3390/asi4010017 - 1 Mar 2021
Cited by 25 | Viewed by 6437
Abstract
Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting [...] Read more.
Efficient Market Hypothesis states that stock prices are a reflection of all the information present in the world and generating excess returns is not possible by merely analysing trade data which is already available to all public. Yet to further the research rejecting this idea, a rigorous literature review was conducted and a set of five technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance—rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG (Facebook, Amazon, Apple, Netflix & Google) equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques, Applications and Developments)
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14 pages, 3197 KiB  
Article
Comparative Analysis of Classification Algorithms Using CNN Transferable Features: A Case Study Using Burn Datasets from Black Africans
by Aliyu Abubakar
Appl. Syst. Innov. 2020, 3(4), 43; https://doi.org/10.3390/asi3040043 - 14 Oct 2020
Cited by 5 | Viewed by 3228
Abstract
Burn is a devastating injury affecting over eleven million people worldwide and more than 265,000 affected individuals lost their lives every year. Low- and middle-income countries (LMICs) have surging cases of more than 90% of the total global incidences due to poor socioeconomic [...] Read more.
Burn is a devastating injury affecting over eleven million people worldwide and more than 265,000 affected individuals lost their lives every year. Low- and middle-income countries (LMICs) have surging cases of more than 90% of the total global incidences due to poor socioeconomic conditions, lack of preventive measures, reliance on subjective and inaccurate assessment techniques and lack of access to nearby hospitals. These factors necessitate the need for a better objective and cost-effective assessment technique that can be easily deployed in remote areas and hospitals where expertise and reliable burn evaluation is lacking. Therefore, this study proposes the use of Convolutional Neural Network (CNN) features along with different classification algorithms to discriminate between burnt and healthy skin using dataset from Black-African patients. A pretrained CNN model (VGG16) is used to extract abstract discriminatory image features and this approach was due to limited burn images which made it infeasible to train a CNN model from scratch. Subsequently, decision tree, support vector machines (SVM), naïve Bayes, logistic regression, and k-nearest neighbour (KNN) are used to classify whether a given image is burnt or healthy based on the VGG16 features. The performances of these classification algorithms were extensively analysed using the VGG16 features from different layers. Full article
(This article belongs to the Special Issue Advanced Machine Learning Techniques, Applications and Developments)
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