Application Of Artificial Intelligence Technology Research Articles

Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.

Application of artificial intelligence in mine ventilation: a brief review

In recent years, there has been a notable integration of artificial intelligence (AI) technologies into mine ventilation systems. A mine ventilation network presents a complex system with numerous interconnected processes, some of which pose challenges for deterministic simulation methods. The utilization of machine learning techniques and evolutionary algorithms offers a promising avenue to address these complexities, resulting in enhanced monitoring and control of air parameter distribution within the ventilation network. These methods facilitate the timely identification of resistance faults and enable prompt calculation of ventilation parameters during emergency scenarios, such as underground explosions and fires. Furthermore, evolutionary algorithms play a crucial role in the advancement of methods for visual analysis of ventilation systems. However, it is essential to acknowledge that the current utilization of AI technologies in mine ventilation is limited and does not encompass the full spectrum of challenging-to-formalize problems. Promising areas for AI application include analyzing changes in air distribution caused by unaccounted thermal draft and gas pressure, as well as developing novel approaches for calculating shock losses. Moreover, the application of AI technologies in optimizing large-scale mine ventilation networks remains an unresolved issue. Addressing these challenges holds significant potential for enhancing safety and efficiency in mine ventilation systems.

Cite space-based research on the use of artificial intelligence in the field of literature and museums

In recent years, with the rapid development and application of artificial intelligence technology, image technology based on artificial intelligence has gradually become a research hotspot in the field of cultural relic restoration. This article will explore the application of artificial intelligence based image technology in the field of cultural relic and museum restoration. Firstly, the importance of restoring cultural relics and artifacts was introduced, as well as the limitations and shortcomings of traditional restoration methods. Secondly, elaborate on the development and application of artificial intelligence technology, as well as its potential and advantages in the field of cultural relic restoration. Next, we will introduce the application of artificial intelligence based image technology in cultural relic restoration, including image enhancement, segmentation, recognition, and reconstruction. Finally, the application prospects and challenges of artificial intelligence based image technology in the field of cultural relic restoration were summarized, and future research directions and suggestions were proposed.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

The Impact of the Application of Artificial Intelligence Technology on the Design Practice of Sports Visual Communication

With the rapid development and application of artificial intelligence (AI) technology, the field of sports visual communication is undergoing unprecedented changes. This paper aims to explore the application of AI technology in the practice of sports visual design, and its impact on visual communication effects and design processes. Through the innovative application of sports visual elements and the optimization of visual design processes, this paper reveals how AI technology can improve the efficiency and quality of sports visual communication, and also points out the challenges and limitations it brings. It is of great significance to promote technological innovation and design improvement in the field of sports visual communication and also provides useful inspiration for exploring the application of AI technology in a wider field of visual design.

Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management

With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve real-time monitoring and early warning, helping banks intervene before risks occur and reduce losses.

Research on the optimization of financial management of financial enterprises

Financial management of financial enterprises is an important part of enterprise management, but also the embodiment of the core competitiveness of enterprises. With the development and application of artificial intelligence technology, the financial management of financial enterprises faces new opportunities and challenges, and it needs to adapt to the changes of the market environment and the diversification of customer needs. Taking Y company as an example, this paper discusses the application status, existing problems and improvement countermeasures of artificial intelligence technology in the financial management of financial enterprises, aiming to provide reference and reference for the financial management of financial enterprises.

The Use of Artificial Intelligence in Employee Recruitment in the Furniture Industry of Iran According to the Role of Contextual Factors

Abstract The present research aims to analyze the effect of contextual factors affecting the application of artificial intelligence technology in employee recruitment in the furniture industry of Iran, which is a practical purpose and has been carried out in a descriptive-surveillance manner, to find out the reasons, facilitators and limitations of its use with the presented conceptual model. Make this technology understandable to organizations during employee recruitment. To measure and analyze the effect of these factors, a questionnaire was used as an information-gathering tool, which was given to 250 senior managers and middle managers of companies active in the furniture industry of Iran. The results of the analysis of the information obtained in two descriptive and inferential parts, according to the data analysis algorithm in the method of structural equations and Smart PLS software, confirmed the hypotheses of the research and showed that the effective background factors include: technological factors, organizational and environmental have a positive and significant effect on the use of Artificial intelligence in the furniture industry in Iran, and the use of artificial intelligence as a competitive advantage improves the organizational capabilities of recruitment and recruitment (based on data, process, staff) in the furniture industry. Forgives. Also, it makes it easier to carry out “blind” Recruitment of employees processes and review frequent applications, and by simplifying the application analysis process through applicant tracking systems, it can save time and money in human resources processes and reduce discrimination in choices.

VAEFL: Integrating Variational Autoencoders for Privacy Preservation and Performance Retention in Federated Learning

Federated Learning (FL) heralds a paradigm shift in the training of artificial intelligence (AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of artificial intelligence technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional Federated Learning methods are vulnerable to Deep Leakage from Gradients (DLG) attacks, and typical defensive strategies often result in excessive computational costs or substantial decreases in model accuracy. To navigate these challenges, this research introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders (VAEs) to bolster privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, offering a secure and efficient federated learning approach for the sensitive application of FL in the financial domain.

The Influence of Artificial Intelligence Technology on College Students' Learning Effectiveness from the Perspective of Constructivism — Taking ChatGPT as an Example

Based on the constructivism theory, this paper takes ChatGPT as an example to discuss how artificial intelligence technology affects the learning effectiveness of college students. Learning in constructivist classrooms is regarded as a proactive process, and the rise of artificial intelligence technology, especially ChatGPT, brings new possibilities to the field of education. In such a context, this paper analyzes the learning effect of students before and after using ChatGPT, paying special attention to a series of deep thinking caused by using ChatGPT. In this paper, 135 students from three different universities in Guangdong Province and Shaanxi Province are selected as the survey participants to investigate their perception toward effective learning before and after using ChatGPT from different angles. The survey results show that using ChatGPT can have a positive impact on their learning efficiency. Finally, this paper discusses the attitudes of different schools to students' use of ChatGPT and puts forward some suggestions to better integrate artificial intelligence technologies such as ChatGPT into higher education. Through in-depth research on the influence of ChatGPT on the learning effectiveness of college students under the constructivism theory, this paper aims to provide high-quality reference for college educators and contribute to the application of artificial intelligence technology in the field of education.

Application of artificial intelligence technology in monitoring students’ health: Preliminary results of Syiah Kuala Integrated Medical Monitoring (SKIMM)

Application of artificial intelligence technology in monitoring students’ health: Preliminary results of Syiah Kuala Integrated Medical Monitoring (SKIMM)

Transformation of Financial Accounting to Management Accounting in the Era of Artificial Intelligence

With the rapid development of the Internet, the era of artificial intelligence has become a trend in today's society, ushering in an intelligent revolution in various industries. The advent of the era of artificial intelligence has laid a solid information foundation and environmental support for the development and progress of the information age, which will greatly impact the traditional working methods and approaches of financial accountants. Basic and repetitive tasks such as issuing invoices and expense reimbursement approvals can be replaced by intelligent machines, allowing financial professionals to focus more on management accounting. The application of artificial intelligence technology can not only improve the efficiency of financial accountants, but also promote the transition from financial accounting to management accounting. Therefore, how to utilize artificial intelligence to assist financial professionals in management accounting work is an inevitable requirement for the development of the intelligent era, which presents more demands for management accounting. Based on this background, the development process of enterprises needs to transform the financial management model, enabling the technical aspects of data collection and organization to move towards more advanced and efficient directions. This article analyzes and studies the strategies for using artificial intelligence to help financial accountants acquire management accounting capabilities, achieve the transformation of financial professionals into management accountants, and provide support for the development of enterprises in the era of artificial intelligence.

The Application of Artificial Intelligence Technology in Shipping: A Bibliometric Review

Artificial intelligence (AI) technologies are increasingly being applied to the shipping industry to advance its development. In this study, 476 articles published in the Science Citation Index Expanded (SCI-EXPANDED) and the Social Sciences Citation Index (SSCI) of the Web of Science Core Collection from 2001 to 2022 were collected, and bibliometric methods were applied to conduct a systematic literature of the field of AI technology applications in the shipping industry. The review commences with an annual publication trend analysis, which shows that research in the field has been growing rapidly in recent years. This is followed by a statistical analysis of journals and a collaborative network analysis to identify the most productive journals, countries, institutions, and authors. The keyword “co-occurrence analysis” is then utilized to identify major research clusters, as well as hot research directions in the field, providing directions for future research in the field. Finally, based on the results of the keyword co-occurrence analysis and the content analysis of the papers published in recent years, the research gaps in AIS data applications, ship trajectory, and anomaly detection, as well as the possible future research directions, are discussed. The findings indicate that AIS data in the future research direction are mainly reflected in the analysis of ship behavior and AIS data repair. Ship trajectory in the future research direction is mainly reflected in the deep learning-based method research and the discussion of ship trajectory classification. Anomaly detection in the future research direction is mainly reflected in the application of deep learning technology in ship anomaly detection and improving the efficiency of ship anomaly detection. These insights offer guidance for researchers’ future investigations in this area. In addition, we discuss the implications of research in the field of shipping AI from both theoretical and practical perspectives. Overall, this review can help researchers understand the status and development trend of the application field of AI technology in shipping, correctly grasp the research direction and methodology, and promote the further development of the field.

A dataset for fine-grained seed recognition

The research of plant seeds has always been a focus of agricultural and forestry research, and seed identification is an indispensable part of it. With the continuous application of artificial intelligence technology in the field of agriculture, seed identification through computer vision can effectively promote the development of agricultural and forestry wisdom. Data is the foundation of computer vision, but there is a lack of suitable datasets in the agricultural field. In this paper, a seed dataset named LZUPSD is established. A device based on mobile phones and macro lenses was established to acquire images. The dataset contains 4496 images of 88 different seeds. This dataset can not only be used as data for training deep learning models in the computer field, but also provide important data support for agricultural and forestry research. As an important resource in this field, this dataset plays a positive role in modernizing agriculture and forestry.

Research on the Influence of Artificial Intelligence on the Employment and Its Countermeasures

Artificial intelligence is the main front to build new quality productive forces and is leading the fourth revolution of science and technology of human beings. The wide application of artificial intelligence technology has triggered people's concerns about unemployment. However, we do not have to worry too much. Artificial intelligence technology will not only replace human work, but also create more job opportunities and higher value-added work. It is imperative to conduct special research on labor force training and re-employment for industries and positions that may be replaced by artificial intelligence in the future, and take the initiative to respond in advance and take comprehensive and effective measures as soon as possible, so as to better adapt to the application and development of artificial intelligence technology.

[Technical Status and Development Trend of Medical Electron Linear Accelerators].

More than 70% of tumor patients require radiotherapy. Medical electron linear accelerators are important high-end radiotherapy equipment for tumor radiotherapy. With the application of artificial intelligence technology in medical electron linear accelerator, radiotherapy has evolved from ordinary radiotherapy to today's intelligent radiotherapy. This study introduces the development history, working principles and system composition of medical electron linear accelerators. It outlines the key technologies for improving the performance of medical linear electron accelerators, including beam control, multi-leaf collimator, guiding technology and dose evaluation. It also looks forward to the development trend of major radiotherapy technologies, such as biological guided radiotherapy, FLASH radiotherapy and intelligent radiotherapy, which provides references for the development of medical electron linear accelerators.

Application of artificial intelligence technology in the automotive field and existing problems

With the development of technology and science, loads of companies try to apply AI to the automotive industry. The automotive industry is at the cusp of a technological revolution, with Artificial Intelligence poised to redefine the way vehicles are designed, operated, and experienced. Nowadays, people still dont have a deep understanding of the application of artificial intelligence in cars. In academia, scholars have briefly analyzed some applications without integrating the known applications. Thus, this paper will integrate multi-dimensional applications, including autonomous driving, car maintenance, and car manufacturing. It will specifically analyze the application and methods of artificial intelligence in automatic driving, the application of automobile maintenance and the practical use and method of AI in the process of automobile manufacturing. Also, it will show the existing problems and challenges that hinder the seamless integration of AI into the automotive industry. The article concludes by providing a summary and outlook for the entire piece.

Использование технологий, основанных на искусственном интеллекте, в преподавании иностранного языка в образовательной организации среднего профессионального образования

The article considers artificial intelligence technology together with deep learning algorithm for modernization of foreign language teaching in the educational organization of secondary vocational education. Foreign lan-guage processing is one of the main methods of artificial intelligence and is widely used in speech analysis and recognition. In order to increase the proportion of qualitatively learned material by young professionals, different teaching methods are implemented according to the different training levels of students, and system-atic simulation comparisons are made according to their degree of training. The work emphasizes the im-portance of a balanced approach to the application of artificial intelligence technologies in the process of teaching foreign language as part of the system of secondary vocational education, taking into account the ad-vantages and disadvantages of software products.

Prospects for the use of artificial intelligence in civil justice

The article discusses the general prospects for the use of artificial intelligence technology in civil proceedings as a main and auxiliary tool at the current stage of the development of this technology. The main approaches to defining the concept of artificial intelligence were studied and its classification into weak, strong and superintelligence was characterized. It is emphasized that justice is usually administered by professional judges, as high demands are placed on their legal knowledge and life experience. It is argued that equal replacement of a judge in civil proceedings with artificial intelligence technology is currently impossible. The reasons for the impossibility of such a replacement are analyzed, namely the complexity and multifacetedness of controversial private-law relations, the impossibility of taking into account moral and ethical aspects, the need for flexibility in decision-making, the problem of the nature of the initial algorithms of artificial intelligence, the transparency (motivation) of decisions, the problem of determining the guilty party in court cases errors, data protection issue. It is noted that these reasons have a technological and legal basis. A critical assessment of the state’s conceptual approaches to the development of artificial intelligence in the field of justice is provided. The directions of the auxiliary application of artificial intelligence technologies in civil proceedings, which are reduced to the automation of routine tasks, processing and analysis of large volumes of data and evidence, and increasing the accessibility of justice, are considered. It has been proven that the use of artificial intelligence technology provides better opportunities for the participants in the judicial process to realize their rights and obligations, including the processing of large amounts of information. The importance of ensuring a high level of information protection when using artificial intelligence during the administration of justice in civil cases is emphasized. The need for regulatory regulation of the use of artificial intelligence technology in civil proceedings in combination with other spheres of public administration and branches of law is substantiated. It is indicated that the wider application of artificial intelligence in civil proceedings will require new technological solutions and revision of the legal foundations of the judicial branch of government.

Evaluations of artificial intelligence and machine learning algorithms in neurodiagnostics.

This article evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI/machine learning (ML) algorithms are analyzed, as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated, as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model's hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility, as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.