CAAI Transactions on Intelligence Technology最新文献

{"title":"Guest Editorial: Knowledge-based deep learning system in bio-medicine","authors":"Yu-Dong Zhang, Juan Manuel Górriz","doi":"10.1049/cit2.12364","DOIUrl":"https://doi.org/10.1049/cit2.12364","url":null,"abstract":"<p>Numerous healthcare procedures can be viewed as medical sector decisions. In the modern era, computers have become indispensable in the realm of medical decision-making. However, the common view of computers in the medical field typically extends only to applications that support doctors in diagnosing diseases. To more tightly intertwine computers with the biomedical sciences, professionals are now more frequently utilising knowledge-driven deep learning systems (KDLS) and their foundational technologies, especially in the domain of neuroimaging (NI).</p><p>Data for medical purposes can be sourced from a variety of imaging techniques, including but not limited to Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET), Magnetic Particle Imaging (MPI), Electroencephalography (EEG), Magnetoencephalography (MEG), Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy.</p><p>Historically, these imaging techniques have been analysed using traditional statistical methods, such as hypothesis testing or Bayesian inference, which often presuppose certain conditions that are not always met. An emerging solution is the implementation of machine learning (ML) within the context of KDLS, allowing for the empirical mapping of complex, multi-dimensional relationships within data sets.</p><p>The objective of this special issue is to showcase the latest advancements in the methodology of KDLS for evaluating functional connectivity, neurological disorders, and clinical neuroscience, such as conditions like Alzheimer's, Parkinson's, cerebrovascular accidents, brain tumours, epilepsy, multiple sclerosis, ALS, Autism Spectrum Disorder, and more. Additionally, the special issue seeks to elucidate the mechanisms behind the predictive capabilities of ML methods within KDLS for brain-related diseases and disorders.</p><p>We received an abundance of submissions, totalling more than 40, from over 10 countries. After a meticulous and rigorous peer review process, which employed a double-blind methodology, we ultimately selected eight outstanding papers for publication. This process ensured the highest standards of quality and impartiality in the selection.</p><p>In the article ‘A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images’, Zebari et al. created a robust deep learning (DL) fusion model for accurate brain tumour classification. To enhance performance, they employed data augmentation to expand the training dataset. The model leveraged VGG16, ResNet50, and convolutional deep belief networks to extract features from MRI images using a softmax classifier. By fusing features from two DL models, the fusion model notably boosted classification precision. Tested with a publicly available dataset, it achieved a remarkable 98.98% accuracy rate, outperforming existing me","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 4","pages":"787-789"},"PeriodicalIF":8.4,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007148","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":"DRRN: Differential rectification & refinement network for ischemic infarct segmentation","authors":"Wenxue Zhou,&nbsp;Wenming Yang,&nbsp;Qingmin Liao","doi":"10.1049/cit2.12350","DOIUrl":"10.1049/cit2.12350","url":null,"abstract":"<p>Accurate segmentation of infarct tissue in ischemic stroke is essential to determine the extent of injury and assess the risk and choose optimal treatment for this life-threatening disease. With the prior knowledge that asymmetric analysis of anatomical structures can provide discriminative information, plenty of symmetry-based approaches have emerged to detect abnormalities in brain images. However, the inevitable non-pathological noise has not been fully alleviated and weakened, leading to unsatisfactory results. A novel differential rectification and refinement network (DRRN) for the automatic segmentation of ischemic strokes is proposed. Specifically, a differential feature perception encoder (DFPE) is developed to fully exploit and propagate the bilateral quasi-symmetry of healthy brains. In DFPE, an erasure-rectification (ER) module is devised to rectify pseudo-lesion features caused by non-pathological noise through utilising discriminant features within the symmetric neighbourhood of the original image. And a differential-attention (DA) mechanism is also integrated to fully perceive the differences in cross-axial features and estimate the similarity of long-range spatial context information. In addition, a crisscross differential feature reinforce module embedded with multiple boundary enhancement attention modules is designed to effectively integrate multi-scale features and refine textual details and margins of the infarct area. Experimental results on the public ATLAS and Kaggle dataset demonstrate the effectiveness of DRRN over state-of-the-arts.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1534-1547"},"PeriodicalIF":8.4,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807294","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 dual benchmarking study of facial forgery and facial forensics","authors":"Minh Tam Pham,&nbsp;Thanh Trung Huynh,&nbsp;Thanh Tam Nguyen,&nbsp;Thanh Toan Nguyen,&nbsp;Thanh Thi Nguyen,&nbsp;Jun Jo,&nbsp;Hongzhi Yin,&nbsp;Quoc Viet Hung Nguyen","doi":"10.1049/cit2.12362","DOIUrl":"10.1049/cit2.12362","url":null,"abstract":"<p>In recent years, visual facial forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as deepfake, fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. However, there is no comprehensive, fair, and unified performance evaluation to enlighten the community on best performing methods. The authors present a systematic benchmark beyond traditional surveys that provides in-depth insights into facial forgery and facial forensics, grounding on robustness tests such as contrast, brightness, noise, resolution, missing information, and compression. The authors also provide a practical guideline of the benchmarking results, to determine the characteristics of the methods that serve as a comparative reference in this never-ending war between measures and countermeasures. The authors’ source code is open to the public.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1377-1397"},"PeriodicalIF":8.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674328","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":"Norm-based zeroing neural dynamics for time-variant non-linear equations","authors":"Linyan Dai,&nbsp;Hanyi Xu,&nbsp;Yinyan Zhang,&nbsp;Bolin Liao","doi":"10.1049/cit2.12360","DOIUrl":"10.1049/cit2.12360","url":null,"abstract":"<p>Zeroing neural dynamic (ZND) model is widely deployed for time-variant non-linear equations (TVNE). Various element-wise non-linear activation functions and integration operations are investigated to enhance the convergence performance and robustness in most proposed ZND models for solving TVNE, leading to a huge cost of hardware implementation and model complexity. To overcome these problems, the authors develop a new norm-based ZND (NBZND) model with strong robustness for solving TVNE, not applying element-wise non-linear activated functions but introducing a two-norm operation to achieve finite-time convergence. Moreover, the authors develop a discrete-time NBZND model for the potential deployment of the model on digital computers. Rigorous theoretical analysis for the NBZND is provided. Simulation results substantiate the advantages of the NBZND model for solving TVNE.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1561-1571"},"PeriodicalIF":8.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141682684","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":"Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture","authors":"Mohammad Hossein Modirrousta,&nbsp;Parisa Forghani Arani,&nbsp;Reza Kazemi,&nbsp;Mahdi Aliyari-Shoorehdeli","doi":"10.1049/cit2.12359","DOIUrl":"https://doi.org/10.1049/cit2.12359","url":null,"abstract":"<p>To gain access to networks, various intrusion attack types have been developed and enhanced. The increasing importance of computer networks in daily life is a result of our growing dependence on them. Given this, it is glaringly obvious that algorithmic tools with strong detection performance and dependability are required for a variety of attack types. The objective is to develop a system for intrusion detection based on deep reinforcement learning. On the basis of the Markov decision procedure, the developed system can construct patterns appropriate for classification purposes based on extensive amounts of informative records. Deep Q-Learning (DQL), Soft DQL, Double DQL, and Soft double DQL are examined from two perspectives. An evaluation of the authors’ methods using UNSW-NB15 data demonstrates their superiority regarding accuracy, precision, recall, and F1 score. The validity of the model trained on the UNSW-NB15 dataset was also checked using the BoT-IoT and ToN-IoT datasets, yielding competitive results.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1467-1484"},"PeriodicalIF":8.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253352","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":"Integer wavelet transform-based secret image sharing using rook polynomial and hamming code with authentication","authors":"Sara Charoghchi,&nbsp;Zahra Saeidi,&nbsp;Samaneh Mashhadi","doi":"10.1049/cit2.12357","DOIUrl":"https://doi.org/10.1049/cit2.12357","url":null,"abstract":"<p>As an effective way to securely transfer secret images, secret image sharing (SIS) has been a noteworthy area of research. Basically in a SIS scheme, a secret image is shared via shadows and could be reconstructed by having the required number of them. A major downside of this method is its noise-like shadows, which draw the malicious users' attention. In order to overcome this problem, SIS schemes with meaningful shadows are introduced in which the shadows are first hidden in innocent-looking cover images and then shared. In most of these schemes, the cover image cannot be recovered without distortion, which makes them useless in case of utilising critical cover images such as military or medical images. Also, embedding the secret data in Least significant bits of the cover image, in many of these schemes, makes them very fragile to steganlysis. A reversible IWT-based SIS scheme using Rook polynomial and Hamming code with authentication is proposed. In order to make the scheme robust to steganalysis, the shadow image is embedded in coefficients of Integer wavelet transform of the cover image. Using Rook polynomial makes the scheme more secure and moreover makes authentication very easy and with no need to share private key to participants. Also, utilising Hamming code lets us embed data with much less required modifications on the cover image which results in high-quality stego images.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"9 6","pages":"1435-1450"},"PeriodicalIF":8.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253346","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":"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":"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}