Applied AI letters最新文献

Fine-Tuned Pretrained Transformer for Amharic News Headline Generation 针对阿姆哈拉语新闻标题生成的微调预训练变换器

Applied AI letters Pub Date : 2024-07-19 DOI: 10.1002/ail2.98

Mizanu Zelalem Degu, Million Meshesha

{"title":"Fine-Tuned Pretrained Transformer for Amharic News Headline Generation","authors":"Mizanu Zelalem Degu, Million Meshesha","doi":"10.1002/ail2.98","DOIUrl":"10.1002/ail2.98","url":null,"abstract":"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.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.98","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TL-GNN: Android Malware Detection Using Transfer Learning TL-GNN：利用迁移学习检测安卓恶意软件

Applied AI letters Pub Date : 2024-05-10 DOI: 10.1002/ail2.94

Ali Raza, Zahid Hussain Qaisar, Naeem Aslam, Muhammad Faheem, Muhammad Waqar Ashraf, Muhammad Naman Chaudhry

{"title":"TL-GNN: Android Malware Detection Using Transfer Learning","authors":"Ali Raza, Zahid Hussain Qaisar, Naeem Aslam, Muhammad Faheem, Muhammad Waqar Ashraf, Muhammad Naman Chaudhry","doi":"10.1002/ail2.94","DOIUrl":"10.1002/ail2.94","url":null,"abstract":"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.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.94","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140990897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building Text and Speech Benchmark Datasets and Models for Low-Resourced East African Languages: Experiences and Lessons 为资源匮乏的东非语言建立文本和语音基准数据集和模型：经验与教训

Applied AI letters Pub Date : 2024-03-26 DOI: 10.1002/ail2.92

Joyce Nakatumba-Nabende, Claire Babirye, Peter Nabende, Jeremy Francis Tusubira, Jonathan Mukiibi, Eric Peter Wairagala, Chodrine Mutebi, Tobius Saul Bateesa, Alvin Nahabwe, Hewitt Tusiime, Andrew Katumba

{"title":"Building Text and Speech Benchmark Datasets and Models for Low-Resourced East African Languages: Experiences and Lessons","authors":"Joyce Nakatumba-Nabende, Claire Babirye, Peter Nabende, Jeremy Francis Tusubira, Jonathan Mukiibi, Eric Peter Wairagala, Chodrine Mutebi, Tobius Saul Bateesa, Alvin Nahabwe, Hewitt Tusiime, Andrew Katumba","doi":"10.1002/ail2.92","DOIUrl":"10.1002/ail2.92","url":null,"abstract":"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.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.92","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140378695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CMD + V for chemistry: Image to chemical structure conversion directly done in the clipboard CMD + V 用于化学：在剪贴板中直接完成图像到化学结构的转换

Applied AI letters Pub Date : 2024-01-25 DOI: 10.1002/ail2.91

Oliver Tobias Schilter, Teodoro Laino, Philippe Schwaller

{"title":"CMD + V for chemistry: Image to chemical structure conversion directly done in the clipboard","authors":"Oliver Tobias Schilter, Teodoro Laino, Philippe Schwaller","doi":"10.1002/ail2.91","DOIUrl":"10.1002/ail2.91","url":null,"abstract":"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.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.91","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139597018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flood susceptibility mapping at the country scale using machine learning approaches 利用机器学习方法绘制国家级洪水易感性地图

Applied AI letters Pub Date : 2023-12-15 DOI: 10.1002/ail2.88

Geoffrey Dawson, Junaid Butt, Anne Jones, Paolo Fraccaro

{"title":"Flood susceptibility mapping at the country scale using machine learning approaches","authors":"Geoffrey Dawson, Junaid Butt, Anne Jones, Paolo Fraccaro","doi":"10.1002/ail2.88","DOIUrl":"https://doi.org/10.1002/ail2.88","url":null,"abstract":"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.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138997858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards bespoke optimizations of energy efficiency in HPC environments 在高性能计算环境中实现能源效率的定制优化

Applied AI letters Pub Date : 2023-12-13 DOI: 10.1002/ail2.87

Robert Tracey, Vadim Elisseev, M. Smyrnakis, Lan Hoang, Mark Fellows, Michael Ackers, Andrew Laughton, Stephen Hill, Phillip Folkes, John Whittle

引用次数: 0

On a quantum inspired approach to train machine learning models 关于训练机器学习模型的量子启发方法

Applied AI letters Pub Date : 2023-12-13 DOI: 10.1002/ail2.89

Jean Michel Sellier

引用次数: 0

Deep aspect extraction and classification for opinion mining in e-commerce applications using convolutional neural network feature extraction followed by long short term memory attention model 基于卷积神经网络特征提取和长短期记忆注意模型的深度方面提取和分类在电子商务应用中的意见挖掘

Applied AI letters Pub Date : 2023-08-09 DOI: 10.1002/ail2.86

Kamal Sharbatian, Mohammad Hossein Moattar

{"title":"Deep aspect extraction and classification for opinion mining in e-commerce applications using convolutional neural network feature extraction followed by long short term memory attention model","authors":"Kamal Sharbatian, Mohammad Hossein Moattar","doi":"10.1002/ail2.86","DOIUrl":"10.1002/ail2.86","url":null,"abstract":"Users of e-commerce websites review different aspects of a product in the comment section. In this research, an approach is proposed for opinion aspect extraction and recognition in selling systems. We have used the users' opinions from the Digikala website (www.Digikala.com), which is an Iranian e-commerce company. In this research, a language-independent framework is proposed that is adjustable to other languages. In this regard, after necessary text processing and preparation steps, the existence of an aspect in an opinion is determined using deep learning algorithms. The proposed model combines Convolutional Neural Network (CNN) and long-short-term memory (LSTM) deep learning approaches. CNN is one of the best algorithms for extracting latent features from data. On the other hand, LSTM can detect latent temporal relationships among different words in a text due to its memory ability and attention model. The approach is evaluated on six classes of opinion aspects. Based on the experiments, the proposed model's accuracy, precision, and recall are 70%, 60%, and 85%, respectively. The proposed model was compared in terms of the above criteria with CNN, Naive Bayes, and SVM algorithms and showed satisfying performance.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.86","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42279959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting mobile money transaction fraud using machine learning algorithms 使用机器学习算法预测移动货币交易欺诈

Applied AI letters Pub Date : 2023-07-12 DOI: 10.1002/ail2.85

Mark E. Lokanan

{"title":"Predicting mobile money transaction fraud using machine learning algorithms","authors":"Mark E. Lokanan","doi":"10.1002/ail2.85","DOIUrl":"10.1002/ail2.85","url":null,"abstract":"The ease with which mobile money is used to facilitate cross-border payments presents a global threat to law enforcement in the fight against money laundering and terrorist financing. This paper aims to utilize machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. The data for this study were obtained from real-time transactions that simulate a well-known mobile transfer fraud scheme. Logistic regression is used as the baseline model and is compared with ensemble and gradient descent models. The results indicate that the logistic regression model still showed reasonable performance while not performing as well as the other models. Among all the measures, the random forest classifier exhibited outstanding performance. The amount of money transferred emerged as the top feature for predicting money laundering transactions in mobile money transfers. These findings suggest that further research is needed to enhance the logistic regression model, and the random forest classifier should be explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.85","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46263497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated patent classification for crop protection via domain adaptation 通过领域适应的作物保护自动专利分类

Applied AI letters Pub Date : 2023-02-15 DOI: 10.1002/ail2.80

Dimitrios Christofidellis, Marzena Maria Lehmann, Torsten Luksch, Marco Stenta, Matteo Manica

{"title":"Automated patent classification for crop protection via domain adaptation","authors":"Dimitrios Christofidellis, Marzena Maria Lehmann, Torsten Luksch, Marco Stenta, Matteo Manica","doi":"10.1002/ail2.80","DOIUrl":"10.1002/ail2.80","url":null,"abstract":"Patents show how technology evolves in most scientific fields over time. The best way to use this valuable knowledge base is to use efficient and effective information retrieval and searches for related prior art. Patent classification, that is, assigning a patent to one or more predefined categories, is a fundamental step towards synthesizing the information content of an invention. To this end, architectures based on Transformers, especially those derived from the BERT family have already been proposed in the literature and they have shown remarkable results by setting a new state-of-the-art performance for the classification task. Here, we study how domain adaptation can push the performance boundaries in patent classification by rigorously evaluating and implementing a collection of recent transfer learning techniques, for example, domain-adaptive pretraining and adapters. Our analysis shows how leveraging these advancements enables the development of state-of-the-art models with increased precision, recall, and F1-score. We base our evaluation on both standard patent classification datasets derived from patent offices-defined code hierarchies and more practical real-world use-case scenarios containing labels from the agrochemical industrial domain. The application of these domain adapted techniques to patent classification in a multilingual setting is also examined and evaluated.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.80","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43702643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0