This paper provides a comprehensive analysis of the evolution and impact of data mining in the field of artificial intelligence (AI), with a particular focus on its application within social and information networks. It traces the origins of AI back to the 1956 Dartmouth Conference, highlighting the subsequent advancements in technologies such as machine learning and data mining that have fueled AI's growth. The paper explores the multifaceted applications of data mining in various sectors including healthcare, transportation, and industrial manufacturing, and delves into the challenges and innovations in recommendation systems, matrix factorization, and intelligent control of autonomous vehicles in intelligent transportation systems. The study emphasizes the significance of distributed algorithms and big data processing frameworks in enhancing the efficiency and applicability of data mining techniques.

Smart agriculture refers to the use of modern information technology, Internet of Things technology, and artificial intelligence to achieve accurate management, efficient operation and sustainable development of the entire process of agricultural production. Smart agriculture mainly includes the application of data collection and analysis, intelligent agricultural machinery, precise fertilization, disease and pest detection and agricultural products traceability. Leaf disease detection and classification are considered as challenging yet important tasks in smart agriculture. Deep leaning methods have been proven effective for these image-based recognition tasks. In this study, two advanced deep learning methods, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined together to achieve a further improvement. Numerical results demonstrate that the proposed method outperforms both CNN and LSTM variants.

This paper delves into the profound impact of Artificial Intelligence (AI) on the film and creative industries, with a focus on AI-driven content creation, audience engagement, market analytics, and the ethical considerations that accompany technological integration. Through detailed analysis of specific applications, such as scriptwriting, visual effects, personalized content, and recommendation systems, the study reveals how AI technologies are reshaping traditional creative processes and audience interaction. It also addresses the implications of AI on employment within creative sectors, intellectual property, authorship rights, and the importance of cultural sensitivity in AI applications. By examining both the opportunities and challenges presented by AI, the paper aims to provide a balanced view on the future of work in creative industries and the ethical framework needed to guide the responsible use of AI technologies.

This study presents a detailed exploration of the application of machine learning (ML) models to optimize investment strategies across various market sectors, including equity markets, fixed income and derivatives, and the volatile cryptocurrency markets. We evaluate three primary ML models: linear regression, decision trees, and neural networks, based on their predictive accuracy, computational efficiency, and robustness to market volatility. A rigorous process involving backtesting and cross-validation assesses each model's performance. Our framework encompasses data preprocessing, feature engineering, model implementation, and a nuanced approach to risk assessment integrating Value at Risk (VaR) and the Sharpe Ratio. We demonstrate the models' effectiveness in predicting stock prices, interest rates, and cryptocurrency price movements. The application of our ML framework led to the development of dynamic portfolio optimization strategies that significantly outperform traditional methods. This study contributes to the understanding of ML's potential to revolutionize investment strategies, providing a foundation for future research and practical applications in financial markets.

This study rigorously examines the application of empirical methodologies aimed at augmenting the user experience (UX) within the domain of human-computer interaction (HCI) design, with a pronounced emphasis on the seamless integration of digital media. In an era where digital media intricately intertwines with our daily lives, crafting interfaces that are both intuitive and engaging is becoming increasingly essential. This paper embarks on an in-depth analysis of a variety of empirical research techniques, including but not limited to, user studies, A/B testing, and comprehensive analytics. These methodologies are pivotal in providing critical insights and feedback that inform and refine the HCI design process. By judiciously incorporating these empirical methods throughout the design and development phases of digital media applications, designers and developers are equipped to forge more effective, accessible, and immersive user experiences. This approach ensures that digital media interfaces are not only functional but also highly engaging and responsive to user needs and preferences. The research findings underscore the critical role of user-centered design practices in significantly enhancing user engagement, satisfaction, and usability. It emphasizes that understanding the end-user's perspective and integrating their feedback into the design process is fundamental in creating digital media interfaces that resonate with users. Through a detailed exploration of these empirical methods, the study provides a comprehensive framework for improving digital media experiences, highlighting the necessity for a synergistic approach to HCI design that prioritizes user satisfaction and usability. This body of work contributes valuable insights into the ongoing discourse on the optimization of digital media interfaces through empirically informed design strategies, advocating for a user-centric approach in the rapidly evolving landscape of digital media technology..

In recent years, computer vision technology has developed rapidly, as one of its important research directions, object detection has received widespread attention due to its high accuracy. Meanwhile, object detection has many application fields, such as intelligent transportation, medical and health, security systems, etc. Traditional object detection methods have limitations when applied to complex real-world scenarios. To improve the shortcomings of conventional methods, deep learning based object detection algorithms have significantly improved the efficiency of object detection and become a research hotspot in object detection. This article summarizes the algorithms into two-stage and one-stage object detection algorithms based on the technical processes and structural differences in handling object detection tasks. Firstly, several common two-stage and one-stage object detection algorithms and their applications in real-world scenarios are introduced. Then their data sets and algorithm performance are analyzed and compared. Finally, according to the results of the comparison, the existing problems of the two-stage object detection algorithm and the one-stage object detection method are discussed, and their future development directions are pointed out.

Autonomous driving technology is one of the most groundbreaking innovations in the automotive industry in recent years. This article will explore the latest trends in autonomous driving, analyzing its historical development, current status, and future prospects, while also assessing its potential across different application scenarios. With the support of data and illustrations, this article aims to provide a comprehensive perspective to understand the development and challenges of this technology. As technology progresses, autonomous driving will significantly enhance traffic safety, efficiency, and convenience, ultimately leading the transportation industry into a new era. Although current autonomous driving systems still face challenges such as boundary identification and safety concerns, ongoing research and innovation across various fields will drive continuous improvements. In the future, autonomous driving is expected to become part of mainstream transportation, not only boosting traffic efficiency but also reducing the workload of drivers and lowering the incidence of traffic accidents. Plans for its widespread global adoption are already underway.

The extensive use of PID controllers in the mechanical field—particularly in robots and other mechanical systems—is covered in this study. In these sectors, PID controllers have become the most popular control technology due to their straightforward design and simplicity of use. PID controller performance in nonlinear complex systems is explored, along with stability and adaptive adjustment, by comparing the conventional and intelligent PID tuning methods. According to the research, control performance can be greatly enhanced by merging PID controller with intelligent technologies like fuzzy logic and genetic algorithms. These results point to the possible use of PID-based control in numerical control systems in the future. PID controllers' automatic tuning capabilities have garnered significant attention in the industrial sector. Future research will focus on the automatic tuning of PID controller to further optimize its performance and broaden its application range.

Embedded Artificial Intelligence (AI) systems are important components of autonomous vehicles. However, incorporating AI into autonomous vehicles is technically complex, due to the constraints of computation, real-time processing of data, uncertainty handling, and hardware limitations. Bayesian Networks (BNs) are a promising method that allows probabilistic modelling in adaptive learning and environment perception. Here we report on an overview of the application of BNs on autonomous driving, with an emphasis on how BNs can be optimized for embedded system resource constraints, including both computational and energy. Various optimization techniques are discussed, such as model pruning, approximation, acceleration using hardware accelerators, such as Field-Programmable Gate Arrays (FPGA) and Application Specific Integrated Circuit (ASICs), and advanced cooling and power management to ensure AI reliability under high computational load. By reviewing these approaches, we aim to contribute to the development of more robust and green AI systems for autonomous driving.

Amidst the swift progression of Internet and big data technologies, recommendation systems have emerged as crucial conduits linking users to products across various digital platforms. This study delves into the deployment of neural collaborative filtering within the realm of movie recommendation systems, with the objective of constructing a system of high precision. Utilizing the MovieLens dataset, this investigation applies one-hot encoding and embedding techniques within the PyTorch-Lightning framework to effectively model user behaviors and predict cinematic preferences. The neural collaborative filtering (NCF) model leverages deep learning to extract latent features of users and items, exhibiting a notable enhancement in performance compared to traditional collaborative filtering approaches. Empirical results indicate that the NCF model attains an accuracy of 86% on the validation dataset, underscoring its efficacy in extensive recommendation scenarios.