CAAI Transactions on Intelligence Technology最新文献

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).

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.

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.

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.

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.

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

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.

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.

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