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A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 35145

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Prof. Dr. Kartik B. Ariyur

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School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA
Interests: predictive maintenance; heath monitoring for ground and aerial vehicles; data analytics; AI; innovation; nonlinear systems analysis and synthesis; adaptation; estimation; filtering; control; general artificial intelligence
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Special Issue Information

Dear Colleagues,

Breakthroughs in 'deep learning' via use of intermediate features in multilayer 'neural networks' and generative adversarial networks using neural networks as generative and discriminative models combined with the massive increase in computing power of GPU chips have resulted in the widespread popularity and use of 'artificial intelligence' in the past decade. The apostrophes in the previous sentence are inserted on purpose to remind the reader that learning, in the biological sense, that improves survival outcomes via biological nervous systems or intelligent decisions improving energy and resource availability are far away from what current software can hope to achieve. The purpose of this Special Issue is to bridge this gap: to develop explanations and an understanding of functioning AI/ML methods, and to develop AI/ML methods that generate outcomes with predictable properties when fed with data satisfying certain conditions.

Thus, it is hoped that the Special Issue will stimulate AI that will increase efficiencies while not compromising safety, trust, fairness, predictability, and reliability when applied to systems with large energy use such as power, water, transport, or financial grids, law and government policy. As a first step towards this goal of transparency of AI algorithms, we seek papers that document the methods so that:

The results are reproducible, at least in the statistical sense;
Algorithms are provided in a common language of sequences of vector matrix algebra operations, which also underlies much deep learning;
Conditions satisfied by data inputs, objective functions of optimization or curve fitting are explicitly listed;
The propagation of data uncertainty to algorithmic outcomes is documented through sensitivity analysis or Monte Carlo simulations.

Potential issues of interest include the following: while there is no repeatability in general in the training of weights in deep learning or most neural networks, there is repeatability in approximating functions or decision boundaries for similar sets of input data. Such results also exist in adaptive control where there is asymptotic tracking without the convergence of parameter estimates. Similarly, a ChatGPT-like AI needs to maintain the consistency of its conclusions, provided the inputs remain consistent. The use of AI in the law can have, for example, quantifiable goals such as the prompt compensation of the victim and long-term reformation of the criminal to higher levels of productivity rather than classical legal outcomes of punishment or retribution, which are subjective. Can a chess or GO GAN handle some level of randomness in the rules of the game?

Dr. Kartik B. Ariyur
Guest Editor

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Published Papers (10 papers)

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Research

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50 pages, 19482 KiB

Open AccessArticle

The Use of eXplainable Artificial Intelligence and Machine Learning Operation Principles to Support the Continuous Development of Machine Learning-Based Solutions in Fault Detection and Identification

by Tuan-Anh Tran, Tamás Ruppert and János Abonyi

Computers 2024, 13(10), 252; https://doi.org/10.3390/computers13100252 - 2 Oct 2024

Viewed by 720

Abstract

Machine learning (ML) revolutionized traditional machine fault detection and identification (FDI), as complex-structured models with well-designed unsupervised learning strategies can detect abnormal patterns from abundant data, which significantly reduces the total cost of ownership. However, their opaqueness raised human concern and intrigued the eXplainable artificial intelligence (XAI) concept. Furthermore, the development of ML-based FDI models can be improved fundamentally with machine learning operations (MLOps) guidelines, enhancing reproducibility and operational quality. This study proposes a framework for the continuous development of ML-based FDI solutions, which contains a general structure to simultaneously visualize and check the performance of the ML model while directing the resource-efficient development process. A use case is conducted on sensor data of a hydraulic system with a simple long short-term memory (LSTM) network. Proposed XAI principles and tools supported the model engineering and monitoring, while additional system optimization can be made regarding input data preparation, feature selection, and model usage. Suggested MLOps principles help developers create a minimum viable solution and involve it in a continuous improvement loop. The promising result motivates further adoption of XAI and MLOps while endorsing the generalization of modern ML-based FDI applications with the HITL concept. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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24 pages, 13967 KiB

Open AccessArticle

Transforming Digital Marketing with Generative AI

by Tasin Islam, Alina Miron, Monomita Nandy, Jyoti Choudrie, Xiaohui Liu and Yongmin Li

Computers 2024, 13(7), 168; https://doi.org/10.3390/computers13070168 - 8 Jul 2024

Cited by 2 | Viewed by 7666

Abstract

The current marketing landscape faces challenges in content creation and innovation, relying heavily on manually created content and traditional channels like social media and search engines. While effective, these methods often lack the creativity and uniqueness needed to stand out in a competitive market. To address this, we introduce MARK-GEN, a conceptual framework that utilises generative artificial intelligence (AI) models to transform marketing content creation. MARK-GEN provides a comprehensive, structured approach for businesses to employ generative AI in producing marketing materials, representing a new method in digital marketing strategies. We present two case studies within the fashion industry, demonstrating how MARK-GEN can generate compelling marketing content using generative AI technologies. This proposition paper builds on our previous technical developments in virtual try-on models, including image-based, multi-pose, and image-to-video techniques, and is intended for a broad audience, particularly those in business management. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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14 pages, 1849 KiB

Open AccessArticle

Using Artificial Intelligence to Predict the Aerodynamic Properties of Wind Turbine Profiles

by Ziemowit Malecha and Adam Sobczyk

Computers 2024, 13(7), 167; https://doi.org/10.3390/computers13070167 - 8 Jul 2024

Viewed by 1288

Abstract

This study describes the use of artificial intelligence to predict the aerodynamic properties of wind turbine profiles. The goal was to determine the lift coefficient for an airfoil using its geometry as input. Calculations based on XFoil were taken as a target for the predictions. The lift coefficient for a single case scenario was set as a value to find by training an algorithm. Airfoil geometry data were collected from the UIUC Airfoil Data Site. Geometries in the coordinate format were converted to PARSEC parameters, which became a direct feature for the random forest regression algorithm. The training dataset included 60% of the base dataset records. The rest of the dataset was used to test the model. Five different datasets were tested. The results calculated for the test part of the base dataset were compared with the actual values of the lift coefficients. The developed prediction model obtained a coefficient of determination ranging from 0.83 to 0.87, which is a good prognosis for further research. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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34 pages, 7324 KiB

Open AccessArticle

The Explainability of Transformers: Current Status and Directions

by Paolo Fantozzi and Maurizio Naldi

Computers 2024, 13(4), 92; https://doi.org/10.3390/computers13040092 - 4 Apr 2024

Cited by 3 | Viewed by 4581

Abstract

An increasing demand for model explainability has accompanied the widespread adoption of transformers in various fields of applications. In this paper, we conduct a survey of the existing literature on the explainability of transformers. We provide a taxonomy of methods based on the combination of transformer components that are leveraged to arrive at the explanation. For each method, we describe its mechanism and survey its applications. We find out that attention-based methods, both alone and in conjunction with activation-based and gradient-based methods, are the most employed ones. A growing attention is also devoted to the deployment of visualization techniques to help the explanation process. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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22 pages, 1949 KiB

Open AccessArticle

A Low-Cost Deep-Learning-Based System for Grading Cashew Nuts

by Van-Nam Pham, Quang-Huy Do Ba, Duc-Anh Tran Le, Quang-Minh Nguyen, Dinh Do Van and Linh Nguyen

Computers 2024, 13(3), 71; https://doi.org/10.3390/computers13030071 - 8 Mar 2024

Cited by 2 | Viewed by 3093

Abstract

Most of the cashew nuts in the world are produced in the developing countries. Hence, there is a need to have a low-cost system to automatically grade cashew nuts, especially in small-scale farms, to improve mechanization and automation in agriculture, helping reduce the price of the products. To address this issue, in this work we first propose a low-cost grading system for cashew nuts by using the off-the-shelf equipment. The most important but complicated part of the system is its “eye”, which is required to detect and classify the nuts into different grades. To this end, we propose to exploit advantages of both the YOLOv8 and Transformer models and combine them in one single model. More specifically, we develop a module called SC3T that can be employed to integrate into the backbone of the YOLOv8 architecture. In the SC3T module, a Transformer block is dexterously integrated into along with the C3TR module. More importantly, the classifier is not only efficient but also compact, which can be implemented in an embedded device of our developed cashew nut grading system. The proposed classifier, called the YOLOv8–Transformer model, can enable our developed grading system, through a low-cost camera, to correctly detect and accurately classify the cashew nuts into four quality grades. In our grading system, we also developed an actuation mechanism to efficiently sort the nuts according to the classification results, getting the products ready for packaging. To verify the effectiveness of the proposed classifier, we collected a dataset from our sorting system, and trained and tested the model. The obtained results demonstrate that our proposed approach outperforms all the baseline methods given the collected image data. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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22 pages, 6565 KiB

Open AccessArticle

Bus Driver Head Position Detection Using Capsule Networks under Dynamic Driving Conditions

by János Hollósi, Áron Ballagi, Gábor Kovács, Szabolcs Fischer and Viktor Nagy

Computers 2024, 13(3), 66; https://doi.org/10.3390/computers13030066 - 3 Mar 2024

Cited by 1 | Viewed by 1729

Abstract

Monitoring bus driver behavior and posture in urban public transport’s dynamic and unpredictable environment requires robust real-time analytics systems. Traditional camera-based systems that use computer vision techniques for facial recognition are foundational. However, they often struggle with real-world challenges such as sudden driver movements, active driver–passenger interactions, variations in lighting, and physical obstructions. Our investigation covers four different neural network architectures, including two variations of convolutional neural networks (CNNs) that form the comparative baseline. The capsule network (CapsNet) developed by our team has been shown to be superior in terms of efficiency and speed in facial recognition tasks compared to traditional models. It offers a new approach for rapidly and accurately detecting a driver’s head position within the wide-angled view of the bus driver’s cabin. This research demonstrates the potential of CapsNets in driver head and face detection and lays the foundation for integrating CapsNet-based solutions into real-time monitoring systems to enhance public transportation safety protocols. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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19 pages, 1275 KiB

Open AccessArticle

Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks

by Vasileios Sevetlidis, George Pavlidis, Spyridon G. Mouroutsos and Antonios Gasteratos

Computers 2024, 13(2), 49; https://doi.org/10.3390/computers13020049 - 8 Feb 2024

Cited by 3 | Viewed by 2255

Abstract

Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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17 pages, 2043 KiB

Open AccessArticle

EfficientNet Ensemble Learning: Identifying Ethiopian Medicinal Plant Species and Traditional Uses by Integrating Modern Technology with Ethnobotanical Wisdom

by Mulugeta Adibaru Kiflie, Durga Prasad Sharma, Mesfin Abebe Haile and Ramasamy Srinivasagan

Computers 2024, 13(2), 38; https://doi.org/10.3390/computers13020038 - 29 Jan 2024

Viewed by 2340

Abstract

Ethiopia is renowned for its rich biodiversity, supporting a diverse variety of medicinal plants with significant potential for therapeutic applications. In regions where modern healthcare facilities are scarce, traditional medicine emerges as a cost-effective and culturally aligned primary healthcare solution in developing countries. In Ethiopia, the majority of the population, around 80%, and for a significant proportion of their livestock, approximately 90% continue to prefer traditional medicine as their primary healthcare option. Nevertheless, the precise identification of specific plant parts and their associated uses has posed a formidable challenge due to the intricate nature of traditional healing practices. To address this challenge, we employed a majority based ensemble deep learning approach to identify medicinal plant parts and uses of Ethiopian indigenous medicinal plant species. The primary objective of this research is to achieve the precise identification of the parts and uses of Ethiopian medicinal plant species. To design our proposed model, EfficientNetB0, EfficientNetB2, and EfficientNetB4 were used as benchmark models and applied as a majority vote-based ensemble technique. This research underscores the potential of ensemble deep learning and transfer learning methodologies to accurately identify the parts and uses of Ethiopian indigenous medicinal plant species. Notably, our proposed EfficientNet-based ensemble deep learning approach demonstrated remarkable accuracy, achieving a significant test and validation accuracy of 99.96%. Future endeavors will prioritize expanding the dataset, refining feature-extraction techniques, and creating user-friendly interfaces to overcome current dataset limitations. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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16 pages, 767 KiB

Open AccessArticle

Constructing the Bounds for Neural Network Training Using Grammatical Evolution

by Ioannis G. Tsoulos, Alexandros Tzallas and Evangelos Karvounis

Computers 2023, 12(11), 226; https://doi.org/10.3390/computers12110226 - 5 Nov 2023

Viewed by 2425

Abstract

Artificial neural networks are widely established models of computational intelligence that have been tested for their effectiveness in a variety of real-world applications. These models require a set of parameters to be fitted through the use of an optimization technique. However, an issue that researchers often face is finding an efficient range of values for the parameters of the artificial neural network. This paper proposes an innovative technique for generating a promising range of values for the parameters of the artificial neural network. Finding the value field is conducted by a series of rules for partitioning the original set of values or expanding it, the rules of which are generated using grammatical evolution. After finding a promising interval of values, any optimization technique such as a genetic algorithm can be used to train the artificial neural network on that interval of values. The new technique was tested on a wide range of problems from the relevant literature and the results were extremely promising. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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Review

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28 pages, 710 KiB

Open AccessReview

A Systematic Review of Using Machine Learning and Natural Language Processing in Smart Policing

by Paria Sarzaeim, Qusay H. Mahmoud, Akramul Azim, Gary Bauer and Ian Bowles

Computers 2023, 12(12), 255; https://doi.org/10.3390/computers12120255 - 7 Dec 2023

Cited by 6 | Viewed by 7384

Abstract

Smart policing refers to the use of advanced technologies such as artificial intelligence to enhance policing activities in terms of crime prevention or crime reduction. Artificial intelligence tools, including machine learning and natural language processing, have widespread applications across various fields, such as healthcare, business, and law enforcement. By means of these technologies, smart policing enables organizations to efficiently process and analyze large volumes of data. Some examples of smart policing applications are fingerprint detection, DNA matching, CCTV surveillance, and crime prediction. While artificial intelligence offers the potential to reduce human errors and biases, it is still essential to acknowledge that the algorithms reflect the data on which they are trained, which are inherently collected by human inputs. Considering the critical role of the police in ensuring public safety, the adoption of these algorithms demands careful and thoughtful implementation. This paper presents a systematic literature review focused on exploring the machine learning techniques employed by law enforcement agencies. It aims to shed light on the benefits and limitations of utilizing these techniques in smart policing and provide insights into the effectiveness and challenges associated with the integration of machine learning in law enforcement practices. Full article

(This article belongs to the Special Issue Deep Learning and Explainable Artificial Intelligence)

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