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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2024) | Viewed by 6015

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Special Issue Editor

Prof. Dr. Yi Ding

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Guest Editor

School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: machine learning; image analysis

Special Issue Information

Dear Colleagues,

Machine perception and learning are highly interdisciplinary and draw on findings in psychology, neuroscience, machine learning, computer vision, and behavioral economics. The mission of this field is to enable machines to perceive and understand the real world in order for them to intelligently generate multimodal content and perform robustly in challenging tasks. Recently, researchers have started to apply a range of machine learning- and AI-based methods to a wide variety of data sources, including multispectral, medical imagery, camera images, live webcam streams and video data. The recurring objective is to design efficient and accurate algorithms for the automatic extraction of semantic information from the data source. There is clear scope for the further development of such approaches to enhance the performance of associated technologies, which is the key aim of this journal, such as machine learning, deep learning, and transfer learning methods and AI models.

We welcome original and well-grounded research papers on all aspects of the foundations of machine perception and learning. The contributions may be theoretical, methodological, algorithmic, empirical, integrative (connecting ideas and methods across machine perception and learning), or critical (e.g., principled analyses and arguments that draw attention to goals, assumptions, or approaches). The submissions should place emphasis on the demonstrated or potential impact of the research in addressing pressing societal challenges, e.g., health, food, environment, education, governance, among others. All submissions will be evaluated and scored for the significance and novelty of the contributions (research problems or questions addressed, methods, experiments, analyses), theoretical and/or empirical soundness of the claims, and clarity of exposition.

The topics of interest include, but are not limited to:

AI-related brain and cognitive science;
Machine perception and human–machine interaction;
Machine learning and data mining;
Multimodal emotion recognition;
Pattern recognition and computer vision;
Signal processing and recognition;
Medical image processing;
Semi-supervised and weakly supervised learning;
Intelligent information processing;
Natural language processing;
Network intelligence and mobile computing;
Intelligent control and decision;
Robotics and intelligent systems;
Auto-ML;
Information fusion from disparate sources.

Prof. Dr. Yi Ding
Guest Editor

Manuscript Submission Information

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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 Sciences 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 2400 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.

Keywords

AI-related brain and cognitive science
machine perception and human–machine interaction
machine learning and data mining
multimodal emotion recognition
pattern recognition and computer vision
signal processing and recognition
medical image processing
semi-supervised and weakly supervised learning
intelligent information processing
natural language processing
network intelligence and mobile computing
intelligent control and decision
robotics and intelligent systems
auto-ML
information fusion from disparate sources

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Published Papers (5 papers)

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Research

17 pages, 3658 KiB

Open AccessArticle

Change and Detection of Emotions Expressed on People’s Faces in Photos

by Zbigniew Piotrowski, Maciej Kaczyński and Tomasz Walczyna

Appl. Sci. 2024, 14(22), 10681; https://doi.org/10.3390/app142210681 - 19 Nov 2024

Viewed by 340

Abstract

Human emotions are an element of attention in various areas of interest such as psychology, marketing, medicine, and public safety. Correctly detecting human emotions is a complex matter. The more complex and visually similar emotions are, the more difficult they become to distinguish. Making visual modifications to the faces of people in photos in a way that changes the perceived emotion while preserving the characteristic features of the original face is one of the areas of research in deepfake technologies. The aim of this article is to showcase the outcomes of computer simulation experiments that utilize artificial intelligence algorithms to change the emotions on people’s faces. In order to detect and change emotions, deep neural networks discussed further in this article were used. Full article

(This article belongs to the Special Issue Machine Perception and Learning)

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14 pages, 3539 KiB

Open AccessArticle

Discrimination Ability and Concentration Measurement Accuracy of Effective Components in Aroma Essential Oils Using Gas Sensor Arrays with Machine Learning

by Toshio Itoh, Pil Gyu Choi, Yoshitake Masuda, Woosuck Shin, Junichirou Arai and Nobuaki Takeda

Appl. Sci. 2024, 14(19), 8859; https://doi.org/10.3390/app14198859 - 2 Oct 2024

Viewed by 703

Abstract

Aroma essential oils contain ingredients that are beneficial to the human body. A gas sensor array is required to monitor the concentration of these essential oil components to regulate their concentration by air conditioning systems. Therefore, we investigated the discrimination ability and concentration measurement accuracy of 14 effective components, including four aroma essential oils (lavender, melissa, tea tree, and eucalyptus), from a single gas sample and mixtures of two gases using sensor arrays. To obtain our data, we used two sensor arrays comprising commercially available semiconductor sensors and our developed semiconductor sensors. For machine learning, principal component analysis was used to visualize the dataset obtained from the sensor signals, and an artificial neural network was used for a detailed analysis. Our developed sensor array, which included sensors that possessed excellent sensor responses to 14 effective components and combined different semiconductive sensor principles, showed a better discrimination and prediction accuracy than the commercially available sensors investigated in this study. Full article

(This article belongs to the Special Issue Machine Perception and Learning)

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21 pages, 3088 KiB

Open AccessArticle

Passive TDOA Emitter Localization Using Fast Hyperbolic Hough Transform

by Gyula Simon and Ferenc Leitold

Appl. Sci. 2023, 13(24), 13301; https://doi.org/10.3390/app132413301 - 16 Dec 2023

Cited by 2 | Viewed by 1141

Abstract

A fast Hough transform (HT)-based hyperbolic emitter localization system is proposed to process time difference of arrival (TDOA) measurements. The position-fixing problem is provided for cases where the source is known to be on a given plane (i.e., the elevation of the source is known), while the sensors can be deployed anywhere in the three-dimensional space. The proposed solution provides fast evaluation and guarantees the determination of the global optimum. Another favorable property of the proposed solution is that it is robust against faulty sensor measurements (outliers). A fast evaluation method involving the hyperbolic Hough transform is proposed, and the global convergence property of the algorithm is proven. The performance of the algorithm is compared to that of the least-squares solution, other HT-based solutions, and the theoretical limit (the Cramér–Rao lower bound), using simulations and real measurement examples. Full article

(This article belongs to the Special Issue Machine Perception and Learning)

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17 pages, 12887 KiB

Open AccessArticle

Deep Neural Network-Based Autonomous Voltage Control for Power Distribution Networks with DGs and EVs

by Durim Musiqi, Vjosë Kastrati, Alessandro Bosisio and Alberto Berizzi

Appl. Sci. 2023, 13(23), 12690; https://doi.org/10.3390/app132312690 - 27 Nov 2023

Cited by 3 | Viewed by 1422

Abstract

This paper makes use of machine learning as a tool for voltage regulation in distribution networks that contain electric vehicles and a large production from distributed generation. The methods of voltage regulation considered in this study are electronic on-load tap changers and line voltage regulators. The analyzed study-case represents a real-life feeder which operates at 10 kV. It has 9 photovoltaic systems with various peak installed powers, 2 electric vehicle charging stations, and 41 secondary substations, each with an equivalent load. Measurement data of loads and irradiation data of photovoltaic systems were collected hourly for two years. Those data are used as inputs in the feeder’s model in DigSilent PowerFactory where Quasi-Dynamic simulations are run. That will provide the correct tap positions as outputs. These inputs and outputs will then serve to train a Deep Neural Network which later will be used to predict the correct tap positions on input data it has not seen before. Results show that ML in general and DNN specifically show usefulness and robustness in predicting correct tap positions with very small computational requirements. Full article

(This article belongs to the Special Issue Machine Perception and Learning)

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18 pages, 4434 KiB

Open AccessArticle

Enhancing Anomaly Detection Models for Industrial Applications through SVM-Based False Positive Classification

by Ji Qiu, Hongmei Shi, Yuhen Hu and Zujun Yu

Appl. Sci. 2023, 13(23), 12655; https://doi.org/10.3390/app132312655 - 24 Nov 2023

Cited by 2 | Viewed by 1858

Abstract

Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%. Full article

(This article belongs to the Special Issue Machine Perception and Learning)

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