Intelligent Technology Research Articles

Employing artificial intelligence technology in developing practical content for media specialization—A case study of Palestine Technical University, Kadoorie

This study investigates the utilization of artificial intelligence (AI) technology to enhance practical content development within the media specialization program at Palestine Technical University, Kadoorie. The primary objective is to examine the extent to which media specialty lecturers employ AI technology in developing practical content. A mixed-methods approach is employed, qualitative data are gathered through in-depth interviews with faculty members to elucidate their perceptions and experiences regarding the integration of AI technology in practical content development. The study aims to provide valuable insights into the benefits and challenges of AI integration in practical content development for media specialization programs The study reveals diverse views on AI integration in media education at Palestine Technical University, Kadoorie. Faculty recognize AI’s benefits like personalized learning and productivity but also express concerns about over-reliance and ethics. Consensus exists on cautious AI implementation to maximize benefits and address drawbacks. Obstacles to AI adoption include cost, skills gaps, and ethical considerations, highlighting the complexity of integration. The study emphasizes a balanced approach, offering insights for enhancing practical content development in media specialization programs at Palestine Technical University, Kadoorie.

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.

A Qualitative Inquiry Into Metacognitive Strategies of Postgraduate Students in Employing ChatGPT for English Academic Writing

ABSTRACTRecent years have witnessed a growing application of generative artificial intelligence (GenAI) technology in writing instruction. Students should mobilise their metacognitive strategies during this endeavour to maximise the benefits of GenAI while avoiding the potential negative impacts. Within the context of tertiary education in Hong Kong, this qualitative research investigates 13 postgraduate students' metacognitive strategies when utilising ChatGPT for English academic writing. Using data collected from audio recordings, semi‐structured interviews and human–GenAI chat logs, we explored students' metacognitive strategies within five dimensions, namely, planning, monitoring, evaluating, information management and debugging. The identified themes and subthemes within each dimension exhibited similarities to those observed in other educational settings. Meanwhile, certain metacognitive strategies (e.g., debugging) were found to be unique to the context of GenAI‐supported academic writing. This study contributes to the literature in two ways. Firstly, it provides a detailed and thorough understanding of metacognitive strategies, complementing the extensive quantitative research in this area. Secondly, this research expands the framework of metacognitive strategies to the context of GenAI‐supported academic writing learning.

Theory of AI-driven scheduling (TAIS): a service-oriented scheduling framework by integrating theory of constraints and AI

This paper introduces the Theory of AI-driven scheduling (TAIS), an innovative framework designed to revolutionise service-oriented scheduling (In the context of this paper, ‘service-oriented’ refers to the design and execution of scheduling processes that are specifically tailored to meet the dynamic and varied needs of service-manufacturing industries. This approach emphasises adaptability, customer-centric scheduling, and the efficient allocation of resources to enhance product and service delivery.) by integrating the theory of constraints (TOC) (see APPENDIX I) with cutting-edge Artificial Intelligence (AI) technologies. TAIS extends the traditional five steps of TOC by introducing three additional layers to enhance adaptability, efficiency, and applicability in service-oriented environments. TAIS incorporates continuous monitoring and adjustment mechanisms across all five TOC steps to improve adaptability, ensuring real-time responsiveness to dynamic scheduling challenges. To enhance efficiency, TAIS integrates lifecycle management and governance into the core framework, which helps in the efficient allocation of resources throughout the scheduling process. Lastly, TAIS includes robust security and compliance measures to bolster applicability. By leveraging AI, TAIS offers a predictive, flexible, and scalable solution capable of addressing the complexities and rapid changes inherent in dynamic scheduling tasks and providing a significant leap forward in scheduling and operations management.

Enhancing IoT Security in Vehicles: A Comprehensive Review of AI-Driven Solutions for Cyber-Threat Detection

Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to industrial machinery. This connectivity allows for a better integration of the pervasive computing, making devices “smart” and capable of interacting with each other and with the corresponding users in a sublime way. However, the widespread adoption of IoT devices has introduced some security challenges, because these devices usually run in environments that have limited resources. As IoT technology becomes more integrated into critical infrastructure and daily life, the need for stronger security measures will increase. These devices are exposed to a variety of cyber-attacks. This literature review synthesizes the current research of artificial intelligence (AI) technologies to improve IoT security. This review addresses key research questions, including: (1) What are the primary challenges and threats that IoT devices face?; (2) How can AI be used to improve IoT security?; (3) What AI techniques are currently being used for this purpose?; and (4) How does applying AI to IoT security differ from traditional methods? Methods: We included a total of 33 peer-reviewed studies published between 2020 and 2024, specifically in journal and conference papers written in English. Studies irrelevant to the use of AI for IoT security, duplicate studies, and articles without full-text access were excluded. The literature search was conducted using scientific databases, including MDPI, ScienceDirect, IEEE Xplore, and SpringerLink. Results were synthesized through a narrative synthesis approach, with the help of the Parsifal tool to organize and visualize key themes and trends. Results: We focus on the use of machine learning, deep learning, and federated learning, which are used for anomaly detection to identify and mitigate the security threats inherent to these devices. AI-driven technologies offer promising solutions for attack detection and predictive analysis, reducing the need for human intervention more significantly. This review acknowledges limitations such as the rapidly evolving nature of IoT technologies, the early-stage development or proprietary nature of many AI techniques, the variable performance of AI models in real-world applications, and potential biases in the search and selection of articles. The risk of bias in this systematic review is moderate. While the study selection and data collection processes are robust, the reliance on narrative synthesis and the limited exploration of potential biases in the selection process introduce some risk. Transparency in funding and conflict of interest reporting reduces bias in those areas. Discussion: The effectiveness of these AI-based approaches can vary depending on the performance of the model and the computational efficiency. In this article, we provide a comprehensive overview of existing AI models applied to IoT security, including machine learning (ML), deep learning (DL), and hybrid approaches. We also examine their role in enhancing the detection accuracy. Despite all the advances, challenges still remain in terms of data privacy and the scalability of AI solutions in IoT security. Conclusion: This review provides a comprehensive overview of ML applications to enhance IoT security. We also discuss and outline future directions, emphasizing the need for collaboration between interested parties and ongoing innovation to address the evolving threat landscape in IoT security.

The Future of Artificial Intelligence and Genetic Insights in Precision Cardiovascular Medicine: A Comprehensive Review

Abstract A medical condition called cardiovascular disease (CVD) affects the heart or blood vessels, and about 40% of its causes can be attributed to genetic factors. The pathophysiology of CVD is still unknown despite numerous studies identifying important environmental and genetic factors. Genetic data research has significantly increased due to the application of genome-wide association studies. The utilization of artificial intelligence (AI) technology demonstrates clear advantages in managing intricate projects, outperforming traditional statistical methods in processing such data. The use of AI in the status of genetic research on CVD and medicine is briefly reviewed in the opening section of this article. Then, it gives a complete picture of how AI is used in genetic CVD research, including genetic data-driven diagnosis and prognosis, genetic variation analysis, gene expression profiles, gene interactions, and analysis of genes using knowledge bases. Even though much research has yielded significant findings, it is still early. The main disadvantages are database limitations, the underuse of AI in systematic biology analysis, and the lack of a theoretical framework for interpreting analysis results. The paper concludes with future directions and the significance of creating comprehensive, high-quality, large-sample-size data-sharing resources. Much research is going into how to use AI analysis techniques to help with development. Being creative with computers can help make new CVD intervention protocols and develop and test theoretical models.

Biomechanics mechanism of sports promoting psychological benefits based on blockchain and intelligent network security

The application of intelligent network technology has realized the transformation of people’s production and lifestyle, and also promoted the transformation of physical education teaching mode. At present, the application of intelligence in the field of sports is becoming more and more powerful. Using the advanced methods and technologies of intelligent network security, this paper aimed to realize the optimization of sports under the security of intelligent network for psychological benefits and study the sports factors that promote psychological benefits. This paper proposed a target tracking algorithm to guide the integration of intelligent network and sports that promote psychological benefits. Using the target tracking in intelligent network security for reference, the biological mechanism of movement promoting psychological benefits was studied to achieve the integration between the two. In the expert evaluation results of the research on sports and psychological benefits of adolescents, creativity and subjective well-being scored the highest and the lowest, with 97.2 and 93.6 points respectively. Therefore, it is very important to use intelligent network security to study the biological mechanism of sports promoting psychological benefits.

Wood Surface-Embedding of Functional Monodisperse SiO2 Microspheres for Achieving Robust, Durable, Nature-Inspired, Programmable Superrepellent Interfaces.

Nature-inspired, robust, durable, liquid-repellent interfaces have attracted considerable interest in the field of wood biomimetic intelligence science and technology application. However, realizing green environmental protection and low maintenance and replacement cost wood surfaces constructed with micro/nanoarchitectures is not an easy task. Aiming at the problem of poor waterproof performance of wood, a silicon dioxide/polydimethylsiloxane (SiO2/PDMS) self-cleaning programmable superhydrophobic coating was biomimetically constructed on the wood substrate by surface-embedded dual-dipping design based on the "substrates + nanoparticles" hybrid principle of the lotus leaf effect. This robust, durable, nature-inspired, self-cleaning, programmable superhydrophobic coating was found to have no observable impact on the original color and texture of the natural wood. The SiO2/PDMS/wood prepared exhibited exceptional liquid repellency and a high static water contact angle (WCA) of 158.5° and a low slide angle (SA) of 10°, including everyday general-purpose droplets, indicating that the introduction of the monodisperse SiO2 microspheres can effectively enhance the superhydrophobic properties of the hydrophilic wood. We applied this strategy to a variety of substrates, including wood-cellulose aerogel and wood-cellulose paper, and demonstrated that the liquid-repellent nature of the self-cleaning superhydrophobic coating remained unchanged. Moreover, the superhydrophobic surface of SiO2/PDMS/wood was preserved even after harsh abrasion conditions, including mechanical damage (sandpaper, sharp steel blade, and tapes), thermal damage (UV irradiation and low/high-temperature exposure such as steaming and freezing), chemical damage, and solvent corrosion (immersion in acid, alkali), demonstrating robust stability of the superhydrophobic coating. Furthermore, the SiO2/PDMS programmable superhydrophobic coating exhibits exceptional exciting self-cleaning and stain-resistant properties, making it offer greater possibilities in terms of scientific challenges and real-world problem-solving at biomimetic smart superhydrophobic interfaces in wood.

A review of the role of artificial intelligence in Journalism

Artificial intelligence (AI) technologies have revolutionized journalism in the digital era. This study is constructed on a general literature review revealing the role of AI in journalism and emphasizes the following key facets: (i) automated reporting, (ii) automated content creation, (iii) automated transcription and translation, (iv) data mining and analysis, (v) fact-checking and verification, and (vi) content personalization. The role of AI is observed in creating news reports like financial digests, sports outcomes, and weather updates, gearing up the automated content creation, transcribing the interviews, providing multilingual support for content translation, data mining and analysis, detecting fake news, personalizing the content in line with audience's preferences. The wide application of AI in journalism automates routine journalistic tasks, thereby improving efficacy and productivity and saving time and effort. Though AI is transforming journalism, there are several challenges facing journalism using AI, including biased algorithms, data availability and quality, data privacy and security, the need for training and education, transparency, and cost concerns. Journalists must be trained to identify and address issues such as data privacy, algorithmic bias, and the ethical implications of adopting AI in news reporting. News agencies should also implement strong data protection measures and transparent AI algorithms to overcome these challenges. They must attain a balance between considering user privacy and offering personalized content. It is paramount to have robust regulatory frameworks to oversee the utility of AI in journalism. Also, warranting ethical standards in AI implementation is crucial to preserving journalistic integrity.

A study on Modeling and Simulation of Sports Training Injury Optimization from a Biomechanical Perspective

With the development of artificial intelligence technology today, sports training is one of the important ways to keep people healthy. Reasonable sports training can keep people happy, and can also improve the physical quality of athletes. However, because the intensity of sports training is greater than the physical endurance, it may lead to physical injury. The sports training injury model can collect the physical data of athletes, and simulate whether the actions that athletes want to practice are standard and correct through the collected data. If there are errors in the simulated exercise actions, the wrong exercise actions can be corrected in time, so as to reduce the incidence of sports injury accidents. The construction of the experimental model and the calculation and analysis process of the construction data are very complex, and the existing simulation models are difficult to achieve this, which has a significant impact on the subsequent development of the simulation model. In view of this phenomenon, this paper, on the basis of the sports training injury model, combined with the neural network method, conducted an effective research on the construction of the sports training injury model, and inspected the performance of the model by testing the accuracy of the evaluation of the injury risk level, the degree of sports training injury, and the accuracy of the evaluation of the prediction results of the sports training injury model. The experimental data shows that the maximum accuracy of the sports training injury model in the horizontal and vertical directions was 94.43% and 95.26% respectively, and the maximum accuracy of the single injury degree and the composite injury degree was 48.68% and 55.01% respectively, which had high accuracy and operational efficiency, and can effectively avoid the occurrence of training injury.

The Law of Data-driven on Employee Growth in Enterprise Human Resource Management in the Era of Digital Transformation

In the era of digital transformation, the popularity of artificial intelligence technology has driven the development of the field of Human Resource Management (HRM) in enterprises, and data-driven HRM has become the key to the development and success of enterprises. Due to the late start, data-driven HRM is still in the development stage in China. To address the problem that traditional HRM lacks accuracy and real-time, this study proposes a Growth Potential Prediction (GPP) model based on the combination of all class features and a Random Forest (RF) classifier. The results of the study showed that the prediction accuracy of interpersonal background, work ability, educational status and organisational environment characteristics under the screening index based on [Formula: see text]1 value were 85.01%, 66.34%, 66.41%, 44.19% and 64.27%, respectively. The accuracy of ROS under the RF classifier and the macro [Formula: see text]1 value were 87.31% and 87.24%. After adding the network features, the accuracy and macro [Formula: see text]1 values of the feature ensemble could reach 90%, and the accuracy and macro [Formula: see text]1 values of all class feature combinations were 89.01% and 89.10%, respectively. In summary, the GPP model based on the combination of all class features and the RF classifier proposed in the study can accurately predict the growth potential of employees and provide some guarantee for mining the growth pattern of employees.

Application of Artificial Intelligence Technology for Prompt Diagnosis of Cast Iron Mechanical Properties

Kinetic indentation is widely used to measure physical and mechanical properties of materials as one of the most universal methods for non-destructive testing. This paper uses the latest advances in artificial intelligence and capabilities of the Python programming language libraries allowing to carry out accurate measurements of cast iron hardness based on the data of the material’s micro-impact loading diagram. It has been shown that use of machine learning allows eliminating gross errors and reducing the error of indirect hardness evaluation in several times – down to 10 units according to Brinell HB. It has also been established that formation of additional features for training models (based on traditionally used characteristics: penetration depths, indenter movement speed and contact forces at certain points in time) has a positive effect on the accuracy of measurements, but amount of measurements should also be optimized. Feasibility of effective use of machine learning to evaluate hardness has been demonstrated by comparing of calculated hardness values with data obtained with standard testing methods. Advantage of the developed testing method is the fact that the developed algorithms can be used for prompt diagnostics of cast iron hardness using existing equipment. It is appropriate to extend the proposed approach for determination of other mechanical properties of cast iron: yield strength, strain hardening index, creep, relaxation, determined by indentation methods.

Design of apple recognition model based on improved deep learning object detection framework Faster-RCNN

It the stage of digital economy development in China, intelligent recognition technology is used in agriculture, forestry and planting industries. This paper improves and optimizes apple recognition based on Faster-RCNN, a deep learning target detection framework, and analyzes the advantages and disadvantages of the improved target detection and recognition model. This study compares and analyzes the conventional Faster-RCNN apple recognition model and the enhanced Faster-RCNN apple recognition model design based on the deep learning target detection framework. From the two groups of recognition model systems under different design schemes, the research and analysis of comprehensive performance, sensitivity and coupling shows that the improved Faster-RCNN Apple recognition model has higher accuracy and more accurate positioning for Apple recognition. The results show that the improved Faster-RCNN apple recognition model can more effectively improve the design of the apple recognition model at the present stage, reduce labor costs, and significantly improve work efficiency. It also promotes the development of the domestic digital economy and provides research support and value for future intelligence and deep learning development paths.

Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach

Abstract The implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception. Graphical abstract

Penerapan Metode Stable Diffusion Dengan Fine Tuning Untuk Pola Endek Bali

Endek Bali fabric is a cultural heritage of Bali renowned for its traditional decorative motifs, including floral, fauna, patra, and diamond patterns. Although rich in cultural value, artisans often face challenges in creating new designs that align with market trends while preserving cultural authenticity. Artificial Intelligence (AI) technology, particularly text-to-image generation models, offers a solution to this issue by streamlining the design process and enabling the exploration of new motifs. The Stable Diffusion model, introduced by Stability.AI in 2022 and open source, can be utilized to generate Endek Bali patterns through Fine Tuning techniques. Fine Tuning allows the model to be adapted to specific domains, enhancing its performance in generating textile patterns based on textual descriptions. This study aims to apply the Stable Diffusion model and Fine Tuning techniques to create new patterns and motifs. By using this model, it is hoped that innovative designs can be produced while maintaining the authenticity and local cultural values of Bali. The research demonstrates that the Fine-Tuned Stable Diffusion model is effective in creating Endek Bali patterns with high accuracy, as evaluated by Clip Similarity, with the highest scores achieved for Floral Patterns (92.43), followed by Decorative (free-form motifs) Floral (88.77), Decorative (free-form motifs) Geometric (87.94), and Decorative (free-form motifs) (85.79). These findings indicate the model’s flexibility and effectiveness in producing intricate textile designs, enabling designers and artisans to generate complex and innovative patterns solely from textual descriptions while preserving Bali’s cultural values.

Navigating the Privacy Paradox : Balancing AI Advancement and Data Protection in the Digital Age

The rapid advancement of artificial intelligence (AI) technologies has ushered in an era of unprecedented data utilization, raising critical concerns about individual privacy. This article examines the complex interplay between AI development and data protection, exploring the challenges of balancing the need for large-scale data processing with the imperative to safeguard personal information. We analyze current regulatory frameworks, including GDPR and CCPA, and their efficacy in addressing AI-specific privacy issues. The article also evaluates technical measures such as differential privacy, federated learning, privacy-preserving machine learning algorithms, and organizational best practices for responsible data management. By synthesizing computer science, law, and ethics perspectives, we propose a multidisciplinary approach to fostering AI innovation while robustly protecting individual privacy rights. Our findings underscore the need for adaptive regulations, enhanced technical safeguards, and increased collaboration between stakeholders to navigate the evolving landscape of AI and privacy in the digital age.

AI Enabled E-commerce Platform

An overview of AI-based e-commerce plat forms and their effects on the online retail sector is given in this survey. Businesses are starting to use artificial intelligence (AI) to improve customer experience, streamline processes, and boost revenue development in the e-commerce industry as a result of the technology’s rapid advancement. This research offers a thorough examination of the state of AI-based e-commerce platforms today, stressing their salient characteristics, advantages, difficulties, and potential. The first part of the poll introduced artificial intelligence (AI) and how it relates to e-commerce. It looks at how artificial intelligence (AI) technologies like computer vision, machine learning, natural language processing, and recommendation systems are being incorporated into e-commerce platforms to provide intelligent customer support, enhance product search and discovery, and offer personalized shopping experiences. Additionally, it explores the role of AI in enhancing customer engagement through chatbots, virtual assistants, and personalized marketing campaigns

Navigating the Future of Pedagogy: The Integration of AI Tools in Developing Educational Assessment Rubrics

ABSTRACTThe advent of artificial intelligence (AI) technologies heralds a transformative era in education. This study investigates the integration of AI tools in developing educational assessment rubrics within the ‘Curriculum Design Development and Evaluation’ course at the University of A Coruña during the 2023–2024 academic year. Employing an action–research methodological approach, 27 comprehensive evaluation rubrics were developed using AI tools like ChatGPT. The findings highlight AI's ability to enhance rubric precision, efficiency and alignment with curricular objectives, thus facilitating personalised learning experiences. This research underscores AI's transformative potential in education, advocating for broader adoption and the necessity for educators to acquire AI proficiency. The study demonstrates how AI tools can streamline the rubric development process, making it more accurate and time efficient while also ensuring the inclusion of essential curricular elements. Through this approach, AI can support innovative assessment strategies that are both effective and adaptable to diverse educational contexts.

Telesurgery: humanitarian and surgical benefits while navigating technologic and administrative challenges.

Telesurgery, the remote execution of surgical procedures through telecommunication and robotic systems, has witnessed substantial growth in recent years, promising to address global healthcare disparities and enhance surgical expertise. This paper explores the humanitarian and surgical benefits of telesurgery, emphasizing its potential to provide expert surgical care to underserved regions. Despite its transformative potential, telesurgery faces significant technologic challenges, including issues of data transmission, latency, and the need for advanced robotic platforms. The advent of 5G networks and innovative robotic systems provides a promising technological landscape, yet global disparities in 5G coverage remain a concern. Ethical considerations, ranging from preserving the surgeon-patient relationship to addressing patient vulnerability and conflicts of interest, are pivotal aspects that demand attention. The paper underscores the importance of clear regulatory frameworks and international collaboration to navigate legal complexities and ensure ethical standards. As telesurgery progresses, integrating artificial intelligence, augmented reality, and haptic feedback technologies holds promise for further advancements. Despite these challenges, telesurgery has the potential to achieve equitable access to expert surgical care; however, it requires a collective effort to overcome its intricate technologic and administrative hurdles.

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observation may overlook. This is particularly valuable for understanding the intricate dynamics of spinal surgery and its multifaceted impacts on patient prognosis. This review aims to delineate the role of artificial intelligence in spinal surgery. A search of the PubMed database from 1992 to 2023 was conducted using relevant English publications related to the application of artificial intelligence in spinal surgery. The search strategy involved a combination of the following keywords: "Artificial neural network," "deep learning," "artificial intelligence," "spinal," "musculoskeletal," "lumbar," "vertebra," "disc," "cervical," "cord," "stenosis," "procedure," "operation," "surgery," "preoperative," "postoperative," and "operative." A total of 1,182 articles were retrieved. After a careful evaluation of abstracts, 90 articles were found to meet the inclusion criteria for this review. Our review highlights various applications of artificial neural networks in spinal disease management, including (1) assessing surgical indications, (2) assisting in surgical procedures, (3) preoperatively predicting surgical outcomes, and (4) estimating the occurrence of various surgical complications and adverse events. By utilizing these technologies, surgical outcomes can be improved, ultimately enhancing the quality of life for patients.