CAAI Transactions on Intelligence Technology最新文献

{"title":"Deep learning on medical image analysis","authors":"Jiaji Wang,&nbsp;Shuihua Wang,&nbsp;Yudong Zhang","doi":"10.1049/cit2.12356","DOIUrl":"https://doi.org/10.1049/cit2.12356","url":null,"abstract":"<p>Medical image analysis plays an irreplaceable role in diagnosing, treating, and monitoring various diseases. Convolutional neural networks (CNNs) have become popular as they can extract intricate features and patterns from extensive datasets. The paper covers the structure of CNN and its advances and explores the different types of transfer learning strategies as well as classic pre-trained models. The paper also discusses how transfer learning has been applied to different areas within medical image analysis. This comprehensive overview aims to assist researchers, clinicians, and policymakers by providing detailed insights, helping them make informed decisions about future research and policy initiatives to improve medical image analysis and patient outcomes.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 1","pages":"1-35"},"PeriodicalIF":8.4,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12356","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A demonstration trajectory segmentation approach for wheelchair-mounted assistive robots","authors":"Mingshan Chi,&nbsp;Yaxin Liu,&nbsp;Qiang Zhang,&nbsp;Chao Zeng","doi":"10.1049/cit2.12358","DOIUrl":"https://doi.org/10.1049/cit2.12358","url":null,"abstract":"<p>Segmentation of demonstration trajectories and learning the contained motion primitives can effectively enhance the assistive robot's intelligence to flexibly reproduce learnt tasks in an unstructured environment. With the aim to conveniently and accurately segment demonstration trajectories, a novel demonstration trajectory segmentation approach is proposed based on the beta process autoregressive hidden Markov model (BP-AR-HMM) algorithm and generalised time warping (GTW) algorithm aiming to enhance the segmentation accuracy utilising acquired demonstration data. This approach first adopts the GTW algorithm to align the multiple demonstration trajectories for the same task. Then, it adopts the BP-AR-HMM algorithm to segment the demonstration trajectories, acquire the contained motion primitives, and establish the related task library. This segmentation approach is validated on the 6-degree-of-freedom JACO robotic arm by assisting users to accomplish a holding water glass task and an eating task. The experimental results show that the motion primitives within the trajectories can be correctly segmented with a high segmentation accuracy.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 3","pages":"738-754"},"PeriodicalIF":8.4,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RailPC: A large-scale railway point cloud semantic segmentation dataset","authors":"Tengping Jiang,&nbsp;Shiwei Li,&nbsp;Qinyu Zhang,&nbsp;Guangshuai Wang,&nbsp;Zequn Zhang,&nbsp;Fankun Zeng,&nbsp;Peng An,&nbsp;Xin Jin,&nbsp;Shan Liu,&nbsp;Yongjun Wang","doi":"10.1049/cit2.12349","DOIUrl":"https://doi.org/10.1049/cit2.12349","url":null,"abstract":"<p>Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value, but its development is severely hindered by the lack of suitable and specific datasets. Additionally, the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories, for example, rail track, track bed etc. To harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation, we introduce RailPC, a new point cloud benchmark. RailPC provides a large-scale dataset with rich annotations for semantic segmentation in the railway environment. Notably, RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning (MLS) point cloud dataset and is the first railway-specific 3D dataset for semantic segmentation. It covers a total of nearly 25 km railway in two different scenes (urban and mountain), with 3 billion points that are finely labelled as 16 most typical classes with respect to railway, and the data acquisition process is completed in China by MLS systems. Through extensive experimentation, we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation results. Based on our findings, we establish some critical challenges towards railway-scale point cloud semantic segmentation. The dataset is available at https://github.com/NNU-GISA/GISA-RailPC, and we will continuously update it based on community feedback.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1548-1560"},"PeriodicalIF":8.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12349","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143252868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things","authors":"Mimonah Al Qathrady,&nbsp;Safi Ullah,&nbsp;Mohammed S. Alshehri,&nbsp;Jawad Ahmad,&nbsp;Sultan Almakdi,&nbsp;Samar M. Alqhtani,&nbsp;Muazzam A. Khan,&nbsp;Baraq Ghaleb","doi":"10.1049/cit2.12352","DOIUrl":"10.1049/cit2.12352","url":null,"abstract":"<p>Industrial Internet of Things (IIoT) is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial environments. Several IIoT nodes operate confidential data (such as medical, transportation, military, etc.) which are reachable targets for hostile intruders due to their openness and varied structure. Intrusion Detection Systems (IDS) based on Machine Learning (ML) and Deep Learning (DL) techniques have got significant attention. However, existing ML and DL-based IDS still face a number of obstacles that must be overcome. For instance, the existing DL approaches necessitate a substantial quantity of data for effective performance, which is not feasible to run on low-power and low-memory devices. Imbalanced and fewer data potentially lead to low performance on existing IDS. This paper proposes a self-attention convolutional neural network (SACNN) architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant features. The proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network (CNN) layers to process the assigned attention features for prediction. The performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID datasets. These datasets encompassed the behaviours of contemporary IIoT communication protocols, the operations of state-of-the-art devices, various attack types, and diverse attack scenarios.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1398-1411"},"PeriodicalIF":8.4,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities","authors":"Shoaib Hassan,&nbsp;Qianmu Li,&nbsp;Khursheed Aurangzeb,&nbsp;Affan Yasin,&nbsp;Javed Ali Khan,&nbsp;Muhammad Shahid Anwar","doi":"10.1049/cit2.12348","DOIUrl":"10.1049/cit2.12348","url":null,"abstract":"<p>Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty-five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K-nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1412-1434"},"PeriodicalIF":8.4,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12348","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial: Special issue on trustworthy machine learning for behavioural and social computing","authors":"Zhi-Hui Zhan,&nbsp;Jianxin Li,&nbsp;Xuyun Zhang,&nbsp;Deepak Puthal","doi":"10.1049/cit2.12353","DOIUrl":"https://doi.org/10.1049/cit2.12353","url":null,"abstract":"&lt;p&gt;Machine learning has been extensively applied in behavioural and social computing, encompassing a spectrum of applications such as social network analysis, click stream analysis, recommendation of points of interest, and sentiment analysis. The datasets pertinent to these applications are inherently linked to human behaviour and societal dynamics, posing a risk of disclosing personal or sensitive information if mishandled or subjected to attacks. To safeguard individuals from potential privacy breaches, numerous governments have enacted a range of legal frameworks and regulatory measures. Examples include the Personal Information Protection Law of the People's Republic of China, the European Union's GDPR for privacy, and Australia's Artificial Intelligence Ethics Framework for many ethical aspects like fairness and reliability. Despite these legislative efforts, the technical implementation of these regulations to ensure trustworthy machine learning in behavioural and social computing remains a significant challenge. Trustworthy machine learning, being a fast-developing field, necessitates further in-depth exploration across multiple dimensions, including but not limited to fairness, privacy, reliability, explainability, robustness, and security, from a holistic and interdisciplinary viewpoint. This special issue is dedicated to facilitating the exchange and discussion of state-of-the-art research findings from academia and industry alike. The seven high-quality papers collected in this special issue place a particular emphasis on showcasing the latest advancements in concepts, algorithms, systems, platforms, and applications, as well as exploring future trends pertinent to the field of trustworthy machine learning for behavioural and social computing.&lt;/p&gt;&lt;p&gt;In the first paper, ‘Trustworthy semi-supervised anomaly detection for online-to-offline logistics business in merchant identification’, Yong Li et al. have developed a semi-supervised framework for the detection of anomalous merchants within the logistics sector. The methodology begins with an extensive data-driven examination comparing the behaviours of regular and anomalous customers. Utilising the insights from this analysis, the authors then implemented a contrastive learning for data augmentation, which capitalises on the imprecise labelling of customer data. Subsequently, their model is employed to identify customers exhibiting abnormal package reception and dispatch patterns in logistics operations. The framework's efficacy is substantiated by an empirical study that leverages 8 months of authentic order data, sourced from Beijing and provided by one of China's foremost logistics corporations.&lt;/p&gt;&lt;p&gt;The second paper, entitled ‘Towards trustworthy multi-modal motion prediction: Holistic evaluation and interpretability of outputs’ by Sandra Carrasco Limeros et al., is advancing toward the creation of dependable motion prediction models, with a focus on the evaluation, robustness, and","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 3","pages":"541-543"},"PeriodicalIF":5.1,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12353","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome","authors":"Gang Wang,&nbsp;Weisheng Li,&nbsp;Mingliang Zhou,&nbsp;Haobo Zhu,&nbsp;Guang Yang,&nbsp;Choon Hwai Yap","doi":"10.1049/cit2.12354","DOIUrl":"10.1049/cit2.12354","url":null,"abstract":"<p>Hypoplastic left heart syndrome (HLHS) is a rare, complex, and incredibly foetal congenital heart disease. To decrease neonatal mortality, evolving HLHS (eHLHS) in pregnant women should be critically diagnosed as soon as possible. However, diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images, making it difficult to rapidly and easily examine for this disease. Herein, the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS (RDeH), which we have named RDeH-Net. Briefly, the framework implements a coarse-to-fine two-stage detection approach, with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains, and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician assistance. The experiments extensively compare the authors’ network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits: (1) it achieved superior average accuracy of 99.37% on three categories of foetal ultrasound images from different cases; (2) it demonstrates visually fine detection performance with weakly supervised localisation. This framework could be used to accelerate the diagnosis of eHLHS, and hence significantly lessen reliance on experienced medical physicians.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1485-1499"},"PeriodicalIF":8.4,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141373007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fault-tolerant and scalable boosting method over vertically partitioned data","authors":"Hai Jiang,&nbsp;Songtao Shang,&nbsp;Peng Liu,&nbsp;Tong Yi","doi":"10.1049/cit2.12339","DOIUrl":"10.1049/cit2.12339","url":null,"abstract":"<p>Vertical federated learning (VFL) can learn a common machine learning model over vertically partitioned datasets. However, VFL are faced with these thorny problems: (1) both the training and prediction are very vulnerable to stragglers; (2) most VFL methods can only support a specific machine learning model. Suppose that VFL incorporates the features of centralised learning, then the above issues can be alleviated. With that in mind, this paper proposes a new VFL scheme, called FedBoost, which makes private parties upload the compressed partial order relations to the honest but curious server before training and prediction. The server can build a machine learning model and predict samples on the union of coded data. The theoretical analysis indicates that the absence of any private party will not affect the training and prediction as long as a round of communication is achieved. Our scheme can support canonical tree-based models such as Tree Boosting methods and Random Forests. The experimental results also demonstrate the availability of our scheme.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1092-1100"},"PeriodicalIF":8.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141384583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WaveSeg-UNet model for overlapped nuclei segmentation from multi-organ histopathology images","authors":"Hameed Ullah Khan,&nbsp;Basit Raza,&nbsp;Muhammad Asad Iqbal Khan,&nbsp;Muhammad Faheem","doi":"10.1049/cit2.12351","DOIUrl":"10.1049/cit2.12351","url":null,"abstract":"<p>Nuclei segmentation is a challenging task in histopathology images. It is challenging due to the small size of objects, low contrast, touching boundaries, and complex structure of nuclei. Their segmentation and counting play an important role in cancer identification and its grading. In this study, WaveSeg-UNet, a lightweight model, is introduced to segment cancerous nuclei having touching boundaries. Residual blocks are used for feature extraction. Only one feature extractor block is used in each level of the encoder and decoder. Normally, images degrade quality and lose important information during down-sampling. To overcome this loss, discrete wavelet transform (DWT) alongside max-pooling is used in the down-sampling process. Inverse DWT is used to regenerate original images during up-sampling. In the bottleneck of the proposed model, atrous spatial channel pyramid pooling (ASCPP) is used to extract effective high-level features. The ASCPP is the modified pyramid pooling having atrous layers to increase the area of the receptive field. Spatial and channel-based attention are used to focus on the location and class of the identified objects. Finally, watershed transform is used as a post processing technique to identify and refine touching boundaries of nuclei. Nuclei are identified and counted to facilitate pathologists. The same domain of transfer learning is used to retrain the model for domain adaptability. Results of the proposed model are compared with state-of-the-art models, and it outperformed the existing studies.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 1","pages":"253-267"},"PeriodicalIF":8.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective interval type-2 fuzzy linear programming problem with vagueness in coefficient","authors":"Shokouh Sargolzaei,&nbsp;Hassan Mishmast Nehi","doi":"10.1049/cit2.12336","DOIUrl":"10.1049/cit2.12336","url":null,"abstract":"<p>One of the most widely used fuzzy linear programming models is the multi-objective interval type-2 fuzzy linear programming (IT2FLP) model, which is of particular importance due to the simultaneous integration of multiple criteria and objectives in a single problem, the fuzzy nature of this type of problems, and thus, its closer similarity to real-world problems. So far, many studies have been done for the IT2FLP problem with uncertainties of the vagueness type. However, not enough studies have been done regarding the multi-objective interval type-2 fuzzy linear programming (MOIT2FLP) problem with uncertainties of the vagueness type. As an innovation, this study investigates the MOIT2FLP problem with vagueness-type uncertainties, which are represented by membership functions (MFs) in the problem. Depending on the localisation of vagueness in the problem, that is, vagueness in the objective function vector, vagueness in the technological coefficients, vagueness in the resources vector, and any possible combination of them, various problems may arise. Furthermore, to solve problems with MOIT2FLP, first, using the weighted sum method as an efficient and effective method, each of the MOIT2FLP problems is converted into a single-objective problem. In this research, these types of problems are introduced, their MFs are stated, and different solution methods are suggested. For each of the proposed methods, the authors have provided an example and presented the results in the corresponding tables.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 5","pages":"1229-1248"},"PeriodicalIF":8.4,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12336","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140983368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}