Applied AI letters最新文献

To create and validate a transformer-based deep neural network architecture for classifying 3D scans of teeth for computer-assisted manufacturing and dental prosthetic rehabilitation surpassing previously reported validation accuracies obtained with convolutional neural networks (CNNs). Voxel-based representation and encoding input data in a high-dimensional space forms of preprocessing were investigated using 34 3D models of teeth obtained from intraoral scanning. Independent CNNs and vision transformers (ViTs), and their combination (CNN and ViT hybrid model) were implemented to classify the 3D scans directly from standard tessellation language (.stl) files and an Explainable AI (ExAI) model was generated to qualitatively explore the deterministic patterns that influenced the outcomes of the automation process. The results demonstrate that the CNN and ViT hybrid model architecture surpasses conventional supervised CNN, achieving a consistent validation accuracy of 90% through three-fold cross-validation. This process validated our initial findings, where each instance had the opportunity to be part of the validation set, ensuring it remained unseen during training. Furthermore, employing high-dimensional encoding of input data solely with 3DCNN yields a validation accuracy of 80%. When voxel data preprocessing is utilized, ViT outperforms CNN, achieving validation accuracies of 80% and 50%, respectively. The study also highlighted the saliency map's ability to identify areas of tooth cavity preparation of restorative importance, that can theoretically enable more accurate 3D printed prosthetic outputs. The investigation introduced a CNN and ViT hybrid model for classification of 3D tooth models in digital dentistry, and it was the first to employ ExAI in the efforts to automate the process of dental computer-assisted manufacturing.

Reservoir models for predicting subsurface fluid and rock behaviors can now include upwards of billions (and potentially trillions) of grid cells and are pushing the limits of computational resources. History matching, where models are updated to match existing historical data more closely, is conducted to reduce the number of simulation runs and is one of the primary time-consuming tasks. As models get larger the number of parameters to match increases, and the number of objective functions increases, and traditional methods start to reach their limitations. To solve this, we propose the use of Bayesian optimization (BO) in a hybrid cloud framework. BO iteratively searches for an optimal solution in the simulations campaign through the refinement of a set of priors initialized with a set of simulation results. The current simulation platform implements grid management and a suite of linear solvers to perform the simulation on large scale distributed-memory systems. Our early results using the hybrid cloud implementation shown here are encouraging on tasks requiring over 100 objective functions, and we propose integrating BO as a built-in module to efficiently iterate to find an optimal history match of production data in a single package platform. This paper reports on the development of the hybrid cloud BO based history matching framework and the initial results of the application to reservoir history matching.

Artificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real-world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.

Amharic is one of the under-resourced languages, making news headline generation particularly challenging due to the scarcity of high-quality linguistic datasets necessary for training effective natural language processing models. In this study, we fine-tuned the small check point of the T5v1.1 model (t5-small) to perform Amharic news headline generation with an Amharic dataset that is comprised of over 70k news articles along with their headline. Fine-tuning the model involves dataset collection from Amharic news websites, text cleaning, news article size optimization using the TF-IDF algorithm, and tokenization. In addition, a tokenizer model is developed using the byte pair encoding (BPE) algorithm prior to feeding the dataset for feature extraction and summarization. Metrics including Rouge-L, BLEU, and Meteor were used to evaluate the performance of the model and a score of 0.5, 0.24, and 0.71, respectively, was achieved on the test partition of the dataset that contains 7230 instances. The results were good relative to result of the t5 model without fine-tuning, which are 0.1, 0.03, and 0.14, respectively. A postprocessing technique using a rule-based approach was used for further improving summaries generated by the model. The addition of the postprocessing helped the system to achieve Rouge-L, BLEU, and Meteor scores of 0.72, 0.52, and 0.81, respectively. The result value is relatively better than the result achieved by the nonfine-tuned T5v1.1 model and the result of previous studies report on abstractive-based text summarization for Amharic language, which had a 0.27 Rouge-L score. This contributes a valuable insight for practical application and further improvement of the model in the future by increasing the article length, using more training data, using machine learning–based adaptive postprocessing techniques, and fine-tuning other available pretrained models for text summarization.

Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state-of-the-art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1-measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.

Africa has over 2000 languages; however, those languages are not well represented in the existing natural language processing ecosystem. African languages lack essential digital resources to effectively engage in advancing language technologies. There is a need to generate high-quality natural language processing resources for low-resourced African languages. Obtaining high-quality speech and text data is expensive and tedious because it can involve manual sourcing and verification of data sources. This paper discusses the process taken to curate and annotate text and speech datasets for five East African languages: Luganda, Runyankore-Rukiga, Acholi, Lumasaba, and Swahili. We also present results obtained from baseline models for machine translation, topic modeling and classification, sentiment classification, and automatic speech recognition tasks. Finally, we discuss the experiences, challenges, and lessons learned in creating the text and speech datasets.

We present Clipboard-to-SMILES Converter (C2SC), a macOS application that directly converts molecular structures from the clipboard. The app focuses on seamlessly converting screenshots of molecules into a desired molecular representation. It supports a wide range of molecular representations, such as SMILES, SELFIES, InChI's, IUPAC names, RDKit Mol's, and CAS numbers, allowing effortless conversion between these formats within the clipboard. C2SC automatically saves converted molecules to a local history file and displays the last 10 entries for quick access. Additionally, it incorporates several SMILES operations, including canonicalization, augmentation, as well price-searching molecules on chemical vendors for the cost-effective purchasing option. Beyond the one-click conversion from clipboard to molecular structures, the app offers continuous monitoring of the clipboard which automatically converts any supported representations or images detected into SMILES. The convenient interface, directly in the status bar, as well as availability as macOS application, makes C2SC useful for a broad community not requiring any programming expertise. Most conversions are performed locally, notably the image-to-SMILES conversion, with internet access only necessary for specific tasks like price lookups. In summary, C2SC provides a user-friendly and efficient solution for converting molecular structures directly from the clipboard, offering seamless conversions between comprehensive chemical representations and can be directly downloaded from https://github.com/O-Schilter/Clipboard-to-SMILES-Converter.

The cover image is based on the Letter Towards bespoke optimizations of energy efficiency in HPC environments by Robert Tracey et al., https://doi.org/10.1002/ail2.87.

River (fluvial), surface water (pluvial) and coastal flooding pose a significant risk to the United Kingdom. Therefore, it is important to assess flood risk particularly as the impacts of flooding are projected to increase due to climate change. Here we present a high resolution combined fluvial and pluvial flood susceptibility map of England. This flood susceptibility model is created by using past flood events and a series of meaningful hydrological parameters to a training machine learning model. We tested the relative performance of different machine learning algorithms, including Classification and Regression Trees, Random Forest and XGBoost and found the XGBoost performed the best, with an area under the receiver operating characteristic ROC Curve (AUC) of 0.93. We also found the model performed well on unseen areas, and we discuss the possibility of extending to regions that has no information on past flood events. Additionally, to aid in understanding what factors may impact flood risk to a particular area, we used Shapley additive explanations which allowed us to investigate the sensitivity of the predicted flood probability to flood factors at a given location.

We present bespoke energy efficiency optimizations in high performance computing (HPC) environments using holistic approach to data collection, analysis and proactive management of resources and workloads. Our solution has three major components: (i) platform for collecting, storing and processing data from multiple sources across hardware and software stacks, (ii) collections of regression machine learning (ML) algorithms for making workloads classifications and energy usage predictions, (iii) agent-based decision-making framework for delivering control decisions to middle-ware and infrastructure thus supporting real time or near real energy efficiency optimizations. We will provide some concrete examples of using our proposed approach in HPC environment.