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AI, Volume 4, Issue 2 (June 2023) – 8 articles

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21 pages, 19994 KiB  
Article
A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
AI 2023, 4(2), 461-481; https://doi.org/10.3390/ai4020025 - 01 Jun 2023
Viewed by 2543
Abstract
Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world [...] Read more.
Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor. Full article
(This article belongs to the Section AI Systems: Theory and Applications)
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24 pages, 2779 KiB  
Review
Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review
AI 2023, 4(2), 437-460; https://doi.org/10.3390/ai4020024 - 23 May 2023
Cited by 3 | Viewed by 3370
Abstract
Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized [...] Read more.
Primary care has the potential to be transformed by artificial intelligence (AI) and, in particular, machine learning (ML). This review summarizes the potential of ML and its subsets in influencing two domains of primary care: pre-operative care and screening. ML can be utilized in preoperative treatment to forecast postoperative results and assist physicians in selecting surgical interventions. Clinicians can modify their strategy to reduce risk and enhance outcomes using ML algorithms to examine patient data and discover factors that increase the risk of worsened health outcomes. ML can also enhance the precision and effectiveness of screening tests. Healthcare professionals can identify diseases at an early and curable stage by using ML models to examine medical pictures, diagnostic modalities, and spot patterns that may suggest disease or anomalies. Before the onset of symptoms, ML can be used to identify people at an increased risk of developing specific disorders or diseases. ML algorithms can assess patient data such as medical history, genetics, and lifestyle factors to identify those at higher risk. This enables targeted interventions such as lifestyle adjustments or early screening. In general, using ML in primary care offers the potential to enhance patient outcomes, reduce healthcare costs, and boost productivity. Full article
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11 pages, 508 KiB  
Article
An Empirical Comparison of Interpretable Models to Post-Hoc Explanations
AI 2023, 4(2), 426-436; https://doi.org/10.3390/ai4020023 - 19 May 2023
Cited by 1 | Viewed by 2307
Abstract
Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretable model, without considering any specifics of the black-box model itself. [...] Read more.
Recently, some effort went into explaining intransparent and black-box models, such as deep neural networks or random forests. So-called model-agnostic methods typically approximate the prediction of the intransparent black-box model with an interpretable model, without considering any specifics of the black-box model itself. It is a valid question whether direct learning of interpretable white-box models should not be preferred over post-hoc approximations of intransparent and black-box models. In this paper, we report the results of an empirical study, which compares post-hoc explanations and interpretable models on several datasets for rule-based and feature-based interpretable models. The results seem to underline that often directly learned interpretable models approximate the black-box models at least as well as their post-hoc surrogates, even though the former do not have direct access to the black-box model. Full article
(This article belongs to the Special Issue Interpretable and Explainable AI Applications)
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20 pages, 5817 KiB  
Article
AI in Energy: Overcoming Unforeseen Obstacles
AI 2023, 4(2), 406-425; https://doi.org/10.3390/ai4020022 - 12 May 2023
Cited by 2 | Viewed by 4985
Abstract
Besides many sectors, artificial intelligence (AI) will drive energy sector transformation, offering new approaches to optimize energy systems’ operation and reliability, ensuring techno-economic advantages. However, integrating AI into the energy sector is associated with unforeseen obstacles that might change optimistic approaches to dealing [...] Read more.
Besides many sectors, artificial intelligence (AI) will drive energy sector transformation, offering new approaches to optimize energy systems’ operation and reliability, ensuring techno-economic advantages. However, integrating AI into the energy sector is associated with unforeseen obstacles that might change optimistic approaches to dealing with AI integration. From a multidimensional perspective, these challenges are identified, categorized based on common dependency attributes, and finally, evaluated to align with the viable recommendations. A multidisciplinary approach is employed through the exhaustive literature to assess the main challenges facing the integration of AI into the energy sector. This study also provides insights and recommendations on overcoming these obstacles and highlights the potential benefits of successful integration. The findings suggest the need for a coordinated approach to overcome unforeseen obstacles and can serve as a valuable resource for policymakers, energy practitioners, and researchers looking to unlock the potential of AI in the energy sector. Full article
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5 pages, 210 KiB  
Communication
Challenges and Limitations of ChatGPT and Artificial Intelligence for Scientific Research: A Perspective from Organic Materials
AI 2023, 4(2), 401-405; https://doi.org/10.3390/ai4020021 - 04 May 2023
Cited by 7 | Viewed by 5906
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology in the scientific community with the potential to accelerate and enhance research in various fields. ChatGPT, a popular language model, is one such AI-based system that is increasingly being discussed and being adapted in [...] Read more.
Artificial Intelligence (AI) has emerged as a transformative technology in the scientific community with the potential to accelerate and enhance research in various fields. ChatGPT, a popular language model, is one such AI-based system that is increasingly being discussed and being adapted in scientific research. However, as with any technology, there are challenges and limitations that need to be addressed. This paper focuses on the challenges and limitations that ChatGPT faces in the domain of organic materials research. This paper will take organic materials as examples in the use of ChatGPT. Overall, this paper aims to provide insights into the challenges and limitations of researchers working in the field of organic materials. Full article
16 pages, 444 KiB  
Article
CAA-PPI: A Computational Feature Design to Predict Protein–Protein Interactions Using Different Encoding Strategies
AI 2023, 4(2), 385-400; https://doi.org/10.3390/ai4020020 - 28 Apr 2023
Viewed by 2008
Abstract
Protein–protein interactions (PPIs) are involved in an extensive variety of biological procedures, including cell-to-cell interactions, and metabolic and developmental control. PPIs are becoming one of the most important aims of system biology. PPIs act as a fundamental part in predicting the protein function [...] Read more.
Protein–protein interactions (PPIs) are involved in an extensive variety of biological procedures, including cell-to-cell interactions, and metabolic and developmental control. PPIs are becoming one of the most important aims of system biology. PPIs act as a fundamental part in predicting the protein function of the target protein and the drug ability of molecules. An abundance of work has been performed to develop methods to computationally predict PPIs as this supplements laboratory trials and offers a cost-effective way of predicting the most likely set of interactions at the entire proteome scale. This article presents an innovative feature representation method (CAA-PPI) to extract features from protein sequences using two different encoding strategies followed by an ensemble learning method. The random forest methodwas used as a classifier for PPI prediction. CAA-PPI considers the role of the trigram and bond of a given amino acid with its nearby ones. The proposed PPI model achieved more than a 98% prediction accuracy with one encoding scheme and more than a 95% prediction accuracy with another encoding scheme for the two diverse PPI datasets, i.e., H. pylori and Yeast. Further, investigations were performed to compare the CAA-PPI approach with existing sequence-based methods and revealed the proficiency of the proposed method with both encoding strategies. To further assess the practical prediction competence, a blind test was implemented on five other species’ datasets independent of the training set, and the obtained results ascertained the productivity of CAA-PPI with both encoding schemes. Full article
(This article belongs to the Special Issue Feature Papers for AI)
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10 pages, 340 KiB  
Commentary
Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology
AI 2023, 4(2), 375-384; https://doi.org/10.3390/ai4020019 - 10 Apr 2023
Cited by 27 | Viewed by 15841
Abstract
ChatGPT is an AI-powered chatbot platform that enables human users to converse with machines. It utilizes natural language processing and machine learning algorithms, transforming how people interact with AI technology. ChatGPT offers significant advantages over previous similar tools, and its potential for application [...] Read more.
ChatGPT is an AI-powered chatbot platform that enables human users to converse with machines. It utilizes natural language processing and machine learning algorithms, transforming how people interact with AI technology. ChatGPT offers significant advantages over previous similar tools, and its potential for application in various fields has generated attention and anticipation. However, some experts are wary of ChatGPT, citing ethical implications. Therefore, this paper shows that ChatGPT has significant potential to transform marketing and shape its future if certain ethical considerations are taken into account. First, we argue that ChatGPT-based tools can help marketers create content faster and potentially with quality similar to human content creators. It can also assist marketers in conducting more efficient research and understanding customers better, automating customer service, and improving efficiency. Then we discuss ethical implications and potential risks for marketers, consumers, and other stakeholders, that are essential for ChatGPT-based marketing; doing so can help revolutionize marketing while avoiding potential harm to stakeholders. Full article
(This article belongs to the Special Issue Standards and Ethics in AI)
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14 pages, 4702 KiB  
Article
FatNet: High-Resolution Kernels for Classification Using Fully Convolutional Optical Neural Networks
AI 2023, 4(2), 361-374; https://doi.org/10.3390/ai4020018 - 03 Apr 2023
Cited by 1 | Viewed by 4328
Abstract
This paper describes the transformation of a traditional in silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels. When using the free-space 4f system to accelerate the inference speed of neural networks, higher resolutions of feature [...] Read more.
This paper describes the transformation of a traditional in silico classification network into an optical fully convolutional neural network with high-resolution feature maps and kernels. When using the free-space 4f system to accelerate the inference speed of neural networks, higher resolutions of feature maps and kernels can be used without the loss in frame rate. We present FatNet for the classification of images, which is more compatible with free-space acceleration than standard convolutional classifiers. It neglects the standard combination of convolutional feature extraction and classifier dense layers by performing both in one fully convolutional network. This approach takes full advantage of the parallelism in the 4f free-space system and performs fewer conversions between electronics and optics by reducing the number of channels and increasing the resolution, making this network faster in optics than off-the-shelf networks. To demonstrate the capabilities of FatNet, it was trained with the CIFAR100 dataset on GPU and the simulator of the 4f system. A comparison of the results against ResNet-18 shows 8.2 times fewer convolution operations at the cost of only 6% lower accuracy. This demonstrates that the optical implementation of FatNet results in significantly faster inference than the optical implementation of the original ResNet-18. These are promising results for the approach of training deep learning with high-resolution kernels in the direction toward the upcoming optics era. Full article
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