Because of the liver tissue, it is difficult and important to diagnose liver diseases. Liver and liver lesions segmentation in medical images with precise automated methods are prerequisites for computer-aided diagnostic systems (CADx). In the liver tissue, benign and malignant tumors are difficult to distinguish because of low contrast. Hence, researchers are trying to provide methods for a more accurate diagnosis. In this chapter, the authors provide an automatic segmentation of the liver and liver lesions in high-contrast CT abdominal images using deep learning and image processing. The deep learning network presented in this chapter is based on the well-known U-Net convolutional neural network powered by the 3-D context information. During the training of the proposed network and U-Net, the authors notice that the network sometimes fluctuates in the early stages of training and abandons the decreasing trend of the cost function and finds an upward jump state.

ABSTRACT To address the problem of the classroom practice teaching of art and design, this paper proposes an interactive learning method in universities, followed by the use of AI technology to evaluate the quality of art and design teaching. The study aims to achieve the following objectives: 1) To introduce the current research status of art and design and interactive teaching methods used in other countries; 2) To discuss the essential ideas and mechanics of Back Propagation Neural Networks (BPNN) and other standard teaching approaches based on interactive learning; and 3) To input test data into the trained model and compare the obtained results with the evaluation results of experts. The findings of this study indicate that the model used to evaluate art and design instruction is accurate. The proposed interactive learning method is beneficial for art and design majors as it allows them to improve their practical skills and learn more engagingly and effectively. The use of AI technology for evaluation purposes can also improve the quality of art and design education in universities.

ABSTRACT Building Information Modeling (BIM) has emerged as a transformative technology in the construction industry, revolutionizing various aspects of the field. The integration of Artificial Intelligence (AI) techniques with BIM holds significant promise and is gaining momentum in interdisciplinary applications. In China, the construction industry has witnessed notable advancements through the convergence of BIM, AI, and cloud data. However, the current state of intelligent construction technology in China reveals certain limitations that hinder its comprehensive development. This study addresses these challenges by focusing on the design of intelligent recognition algorithms for monitoring structural damage during bridge construction. Previous research has primarily employed classical neural network algorithms, but these approaches have exhibited certain limitations. This paper proposes innovative improvement measures to overcome these limitations and demonstrates their effectiveness through practical arithmetic examples. Furthermore, to enhance the intelligence level of the BIM system, this study integrates the improved neural network recognition algorithm into the BIM framework. The integration enables the BIM system to recognize and assess bridge structure damage efficiently and accurately. The outcomes of this research provide valuable insights into advancing the field of intelligent construction technology, particularly in the context of bridge construction monitoring.

ABSTRACT Mobile devices, including phones, tablets, and smartwatches, have revolutionized the way we compute and have seamlessly integrated into education systems. These versatile devices store vast amounts of valuable personal data due to their rich user interactions and advanced sensor capabilities. By harnessing the potential of this data through model training, we can greatly enhance the functionality and effectiveness of smart applications, providing educators with invaluable insights for making informed decisions. However, it is crucial to acknowledge the significant risks and responsibilities associated with handling such sensitive information. One notable breakthrough in this field is distributed machine learning, which enables improved accuracy and scalability by employing a multi-node system. This approach is particularly advantageous for processing larger input data sizes, allowing for enhanced performance and reduced errors. Moreover, it facilitates assisting individuals in making well-informed choices and effectively analyzing extensive datasets. This work introduces an advanced distributed intelligent model that leverages fully distributed machine learning techniques. Through a consensus mechanism and the exchange of gradients, we ensure the utmost integrity of private data pertaining to sports activities, education, training, and the health of preschoolers. The robust privacy and security features of this model make it an ideal solution for preschool organizations and educational institutions seeking to harness the power of machine learning while upholding the strictest standards of data privacy and security.

ABSTRACT The integration of Artificial Intelligence (AI) into the financial industry has witnessed significant growth, transitioning from an academic concept to widespread adoption in the industrial sector. This trend has given rise to various AI technologies, presenting both novel opportunities and potential risks within the financial landscape. In light of this development, the present research article aims to investigate the expanding role of AI in the financial sector, focusing on its applications and impact across financial products, channels, and service methodologies. To accomplish this objective, a specific AI algorithm called Support Vector Machine for Regression (SVR) has been selected for analysis. The SVR algorithm is particularly well-suited for small sample learning, making it an appropriate choice for examining trends in Shibor. By employing a combination of theoretical analysis, case studies, and risk assessment, this article contributes to fostering a profound and robust integration of AI and finance. Consequently, it delivers both theoretical insights and practical significance, offering valuable knowledge for industry practitioners and academic researchers alike.

ABSTRACT Artificial intelligence (AI) and big data profoundly impact people’s way of life and way of thinking, and college ideological and political education (IPE) has gradually entered the era of online education. On account of this, this study designs an online education algorithm based on AI technology to help teachers better understand the situation of students’ online IPE teaching and improve the management of IPE in universities. Firstly, the learning features of students are extracted through the Back Propagation Neural Network (BP) model. This model summarizes the shortcomings of feature extraction in machine learning, and can simultaneously obtain depth information from the signals of multiple sensors, thus increasing the overall algorithm classification accuracy. Secondly, combined with the human behavior recognition model, the status and behavior of students’ IPE teaching can be obtained in real-time from students’ listening devices. Finally, the algorithm’s classification performance is evaluated by experiments and compared with the designed model. The results reveal that the recognition accuracy of the designed classification algorithms for the sample students is 98.59%, 98.99%, 99.21%, 100%, 97.10%, 95.61%, and 100%, respectively. In addition, comparing the algorithm with similar recognition algorithms, its index values of accuracy and precision are 97.83% and 97.82%, respectively, which are better than similar classification algorithms. Finally, through the experimental samples, the accuracy of the human recognition model is tested and compared with other recognition models. The results reveal that the designed model has high recognition accuracy. This study is of great significance for improving teachers’ innovative IPE methods and optimizing the management level of online IPE teaching.

ABSTRACT The finite volume neural network (FINN) is an exception among recent physics-aware neural network models as it allows the specification of arbitrary boundary conditions (BCs). FINN can generalize and adapt to various prescribed BC values not provided during training, where other models fail. However, FINN depends explicitly on given BC values and cannot deal with unobserved parts within the physical domain. To overcome these limitations, we extend FINN in two ways. First, we integrate the capability to infer BC values on-the-fly from just a few data points. This allows us to apply FINN in situations, where the BC values, such as the inflow rate of fluid into a simulated medium, are unknown. Second, we extend FINN to plausibly reconstruct missing data within the physical domain via a gradient-driven spin-up phase. Our experiments validate that FINN reliably infers correct BCs, but also generates smooth and plausible full-domain reconstructions that are consistent with the observable data. Moreover, FINN can generate precise predictions orders of magnitude more accurate compared to competitive pure ML and physics-aware ML models – even when the physical domain is only partially visible, and the BCs are applied at a point that is spatially distant from the observable volumes.

ABSTRACT It is true that with society entering a new era, the pressure of competition for employment of college students is increasing, and society has higher requirements for the quality of high-tech talents, which can result in mental health issues for college students. Therefore, focusing on psychological education for college students and improving their psychological quality is essential. The analysis and countermeasure research on psychological characteristics has become crucial in this context. To address this issue, this work proposes an Improved Particle Swarm Optimization (IPSO) Deep Belief Network (DBN) method to analyze the psychological characteristics of college students. The proposed method first uses DBN to analyze the psychological characteristics of college students. Secondly, the learning factors and inertia weights in PSO are dynamically adjusted to construct IPSO to address the problem of slow overall convergence speed caused by the initial randomization of weights in the DBN training process. IPSO is then combined with DBN to adjust the initial weights for DBN training. This work also proposes a series of countermeasures from different aspects to strengthen mental health education for college students. The validity of IPSO-DBN for college students’ psychological characteristics analysis is verified through comprehensive and systematic experiments, and the feasibility of college students’ mental health education countermeasures is confirmed via comparison. Overall, this work provides an innovative and effective approach for analyzing college students’ psychological characteristics and developing countermeasures to improve their mental health. It shows the potential of combining artificial intelligence with psychological education and highlights the importance of strengthening psychological health education for college students.

ABSTRACT To improve the teaching effect of the Chinese language and literature majors, this paper combines artificial intelligence technology and performance to build a teaching system for the Chinese language and literature majors. Based on the existing coordination algorithms, this paper proposes a classifier-based fast calculation method for successive breaking of active power flow, which provides high-quality active power information for the static safety check and control of successive breaking at a small computational cost. Moreover, this paper improves the operation effect of the teaching system, increases the calculation speed, and adopts the NR method for algorithm-sensitive faults to ensure the reliability of the power flow results. The proposed teaching system was tested in a simulated teaching environment to evaluate its effectiveness. The testing results showed that the system significantly improved student performance, engagement, and satisfaction. However, some challenges and limitations were also encountered during the testing process, such as the need for sufficient computing power and the difficulty in accommodating individual learning styles. The findings of this paper suggest that the proposed teaching system has the potential to enhance the learning outcomes of Chinese language and literature students. However, further testing and optimization are needed to address the challenges and limitations encountered in the testing process.

ABSTRACT Process mining uses event logs to improve business processes, but such logs may contain privacy information. One popular research problem is the privacy protection of event logs. Publishing logs with differential privacy is one of major research directions. Existing research achieves privacy protection primarily by injecting random noise into event logs, or merging similar information. The former ignores the fact that injecting random noise will produce apparently unreasonable activity traces, and the latter will cause a loss of process information in the process mining perspective. To solve the above problems, this article proposes a differential algorithm based on randomized response to model Petri nets for the original event logs, select the important labels in the logs by the weak sequential relationship of control flow between activities, inject noise into the Petri net model based on the important labels using the randomized response approach, and establish a differential Petri net model. Experiments on public datasets show that the event logs produced by the approach proposed do not contain unreasonable traces. Compared with the baseline approach, the proposed approach performs better on Fitness metrics with consistent privacy requirements and retains more process variants, reducing the loss of original event log process information.