Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

In the fire scene investigation, the firefighting Internet of Things (IoT) data is the key electronic evidence for event analysis and responsibility determination. However, the traditional centralized storage method leads to data easy to be tampered with and damaged. To solve these problems, this paper designs and implements a secure, reliable and low-cost distributed firefighting IoT data storage scheme based on the Fabric framework, combining blockchain technology, Interplanetary File System (IPFS) and Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to provide a strong support for fire accident traceability. This scheme mainly includes the storage model, key algorithms and Fabric construction and improvement. IPFS stores the complete firefighting IoT data, as the off-chain storage system of the blockchain, and the blockchain only stores the storage address (IPFS hash) of data returned by IPFS, thus reducing the storage space overhead of the blockchain and ensuring data security. Further, we adopt the Fabric framework as the blockchain platform for firefighting IoT data, and embed the PBFT consensus algorithm into the framework to ensure the reliability of consensus nodes in Fabric, thus improving the availability of the blockchain. In addition, we use the AES and RSA algorithms to ensure the security of firefighting IoT data storage and transmission. Through system analysis and experimental testing, the proposed scheme meets the need for secure storage and traceability of firefighting IoT data. Compared with the storage scheme using only blockchain, the blockchain combined with IPFS technology has advantages in storage space occupation, significantly improved throughput, and lower latency overhead. Meanwhile, compared with the official Fabric, the improved Fabric supports Byzantine fault tolerance and has better security.

Weibo is one of the most important online social platforms. At present, user comments are increasing rapidly, which brings the problem of difficult management. The comments show the non-standardized and colloquial form of expression. Traditional sentiment analysis techniques are no longer applicable to the unspecified sentence analysis tasks. To address the problems of traditional classification models over-relying on text sequences, ignoring syntactic structure, and the poor interpretability of feature space, in this paper, a sentiment classification model based on Graph Neural Network (GNN) is proposed. For each comment text, the dependency syntax is used to construct the semantic graph of the short text; aiming at the heterogeneity of semantic graph, the spatial domain graph filter is designed for feature extraction; at the same time, Long Short-Term Memory (LSTM) is introduced as a state updater to filter node noise. In this method, Graph Neural Network is introduced as semantic parser to encode the syntactic dependency tree, which can extract the semantic and syntactic features of sentences at the same time. The experimental results show that GNN-LSTM has achieved superior performance inWeibo comments dataset by achieving 95.25% accuracy and 95.22% F1 score.

Recognizing students’ behaviors in classes based on videos of their activity plays an important role in improving teaching quality and paying attention to the healthy growth of students. The existing student behavior recognition methods mainly focus on the behavior of the single student and have low performance and efficiency. To recognize the behaviors of multiple students in the classroom at the same time, we propose a fast and effective solution, called ET-YOLOv5s, which is an improved YOLOv5s with ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) and a tiny object detection module in this paper. First, the ESRGAN is adopted to generate high-definition images from original images of real classroom environments in colleges to improve the recognition accuracy. Then a tiny object detection module is added to the YOLOv5s to detect the smaller objects on high-definition images, such as phones, books, and students sitting at the back of the classroom. Finally, the experimental results show that the proposed solution can detect 11 kinds of students’ behaviors in classrooms, including playing mobile phones, having class, sitting with hands on face, sitting turn right, sitting turn left, bowing, sleeping, and so on. Compared with YOLOv4, YOLOv5s, and other tiny target detection algorithms, it has better detection performance and can effectively deal with the behavior recognition of multiple students.

In large public places such as railway stations and airports, dense pedestrian detection is important for safety and security. Deep learning methods provide relatively effective solutions but still face problems such as feature extraction difficulties, image multi-scale variations, and high leakage detection rates, which bring great challenges to the research in this field. In this paper, we propose an improved dense pedestrian detection algorithm GR-yolo based on Yolov8. GR-yolo introduces the repc3 module to optimize the backbone network, which enhances the ability of feature extraction, adopts the aggregation–distribution mechanism to reconstruct the yolov8 neck structure, fuses multi-level information, achieves a more efficient exchange of information, and enhances the detection ability of the model. Meanwhile, the Giou loss calculation is used to help GR-yolo converge better, improve the detection accuracy of the target position, and reduce missed detection. Experiments show that GR-yolo has improved detection performance over yolov8, with a 3.1% improvement in detection means accuracy on the wider people dataset, 7.2% on the crowd human dataset, and 11.7% on the people detection images dataset. Therefore, the proposed GR-yolo algorithm is suitable for dense, multi-scale, and scene-variable pedestrian detection, and the improvement also provides a new idea to solve dense pedestrian detection in real scenes.

Since the position of the classroom surveillance camera is not fixed, the angle of the face captured through the surveillance video is also different. The deep learning-based face verification model has achieved good results in controlled environments, but there is still much room for improvement in the face verification ability in uncontrolled environments. The performance of the model depends not only on the results of the network but also on the quality and diversity of the dataset. The current Asian face dataset in an uncontrolled environment is insufficient; for this reason, this paper constructs a Chinese face dataset (UCEC-Face) in an uncontrolled classroom environment, which is collected by 35 real classroom surveillance videos. The UCEU dataset contains 7395 images of 130 subjects, including 44 males and 86 females. To verify that there is still room for improving the performance of existing face verification models for Asian face verification, we further utilize four models such as OpenFace and ArcFace for face verification, as well as the VGG-Face model for gender, expression, and age recognition on the UCEC-Face. The experimental results show that the UCEC-Face constructed in this paper is more challenging and difficult to verify in face verification tasks because it is closer to the real environment, and the best results obtained on the existing models only reach 69.7%, which is largely below the average accuracy of the identification results of other datasets.