Development Of Artificial Intelligence Research Articles

Applications of Lightsheet Fluorescence Microscopy by High Numerical Aperture Detection Lens.

This Review explores the evolution, improvements, and recent applications of Light Sheet Fluorescence Microscopy (LSFM) in biological research using a high numerical aperture detection objective (lens) for imaging subcellular structures. The Review begins with an overview of the development of LSFM, tracing its evolution from its inception to its current state and emphasizing key milestones and technological advancements over the years. Subsequently, we will discuss various improvements of LSFM techniques, covering advancements in hardware such as illumination strategies, optical designs, and sample preparation methods that have enhanced imaging capabilities and resolution. The advancements in data acquisition and processing are also included, which provides a brief overview of the recent development of artificial intelligence. Fluorescence probes that were commonly used in LSFM will be highlighted, together with some insights regarding the selection of potential probe candidates for future LSFM development. Furthermore, we also discuss recent advances in the application of LSFM with a focus on high numerical aperture detection objectives for various biological studies. For sample preparation techniques, there are discussions regarding fluorescence probe selection, tissue clearing protocols, and some insights into expansion microscopy. Integrated setups such as adaptive optics, single objective modification, and microfluidics will also be some of the key discussion points in this Review. We hope that this comprehensive Review will provide a holistic perspective on the historical development, technical enhancements, and cutting-edge applications of LSFM, showcasing its pivotal role and future potential in advancing biological research.

INVOLVEMENT OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE

With the coming of the digital age, there is a compelling need to integrate Artificial intelligence (AI) with healthcare and other aspects of social development which will improve well-being and the quality of life of the population. Availability to the average users, rather than only rich organizations, will help revolutionize healthcare in a more rapid manner. AI technologies are largely developed by high income countries, whereas their use in low-income countries is urgent. AI is thus needed to make a transformation in overall health systems and not to be seen as only a technological solution to solve any one health problem. With innovative and sustainable development of AI in the health sector, we can come close to achieving reliable and quality universal health care for all.

Искусственный интеллект глазами российских студентов: настоящее и будущее

The study, which involves 265 respondents, is aimed at identifying student youth’s attitude to using artificial intelligence in various spheres of life and ideas about it. To collect data, the authors use a questionnaire, including open questions and multiple-choice tasks in its main part. To process the data the paper applies content analysis, descriptive statistics and Pearson’s 2 criterion. It is found that students understand the phenomenon of artificial intelligence differently, interpreting it as an imitator of the human brain, a tool or a practice-oriented solution, a certain algorithm or scientific knowledge. Some students do not clearly understand the essence of the term “artificial intelligence” or find it difficult to interpret it. Students associate the main functionality of artificial intelligence with the increased labour or study productivity, with assistance in performing routine; less often, they associate it with creative tasks. However, some students cannot determine the functionality of artificial intelligence or do not see any prospects for its development for humans. Most students, considering artificial intelligence as their assistant, which reduces the time of obtaining data and creates certain conveniences, turn to it to solve their problems often or rarely. They are ready to use only free services or determine the budget for such expenses taking into account the tasks they are to solve. Students find the main barriers to using artificial intelligence in the issuance of standard, stereotypical solution options and the limitation on the data volume. Students see the prospects for the artificial intelligence development in its limitation in the legal field and possible superiority over the human mind. It is advisable to include knowledge of the artificial intelligence concept and the ability to determine its functionality in the list of universal competencies mastered at the university, and understanding the essence of artificial intelligence and the ability to use it in the context of solving applied problems should be included in professional competencies.

A comprehensive review of artificial intelligence for pharmacology research.

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

The Dilemmas and Solutions in the Application of Criminal Law to Property Crimes in the Age of Artificial Intelligence

With the rapid development of Artificial Intelligence (AI) technology, the forms and methods of property crimes have undergone significant changes. AI has not only enhanced the capabilities of criminals but also increased the concealment and complexity of criminal activities. These changes pose new challenges to the existing criminal law system. This paper explores the main characteristics of property crimes in the AI era, analyzes the dilemmas encountered in the application of criminal law, including legal lag, difficulties in evidence collection, and technological barriers. In response to these dilemmas, the paper proposes corresponding countermeasures, including improving the legal system, innovating legislation, enhancing technical support, and promoting international cooperation. By analyzing these issues and solutions, this paper aims to provide useful references and suggestions for the application of criminal law to property crimes in the age of AI.

Capturing the unobservable in AI development: proposal to account for AI developer practices with ethnographic audit trails (EATs)

AbstractThe prevalence of artificial intelligence (AI) tools has inspired social studies researchers, ethicists, and policymakers to seriously examine AI’s sociopolitical and ethical impacts. AI ethics literature provides guidance on which ethical principles to implement via AI governance; AI auditing literature, especially ethics-based auditing (EBA), suggests methods to verify if such principles are respected in AI model development and deployment. As much as EBA methods are abundant, I argue that most currently take a top-down and post-hoc approach to AI model development: Existing EBA methods mostly assume a preset of high-level, abstract principles that can be applied universally across contexts; meanwhile, current EBA is only conducted after the development or deployment of AI models. Taken together, these methods do not sufficiently capture the very developmental practices surrounding the constitution of AI models on a day-to-day basis. What goes on in an AI development space and the very developers whose hands write codes, assemble datasets, and design model architectures remain unobserved and, therefore, uncontested. I attempt to address this lack of documentation on AI developers’ day-to-day practices by conducting an ethnographic “AI lab study” (termed by Florian Jaton), demonstrating just how much context and empirical data can be excavated to support a whole-picture evaluation of AI models’ sociopolitical and ethical impacts. I then propose a new method to be added to the arsenal of EBA: Ethnographic audit trails (EATs), which take a bottom-up and in-progress approach to AI model development, capturing the previously unobservable developer practices.

Analisis Kelebihan dan Kekurangan Media Belajar AI dalam Proses Pembelajaran PAI di Perguruan Tinggi

AI is used by humans to help simplify and complete tasks in everyday life. AI is also used in the world of education. Important research is carried out because the development of artificial intelligence (Artificial Intelligence) has changed many aspects of human life, especially the world of education. In the digital era, Artificial Intelligence plays an important role in helping lecturers and students face learning challenges and achieve academic success. The research results show that AI media has several advantages in the PAI learning process in higher education, namely: 1) Data Management Efficiency, 2) Personalization of Learning, 3) Effective Feedback, 4) Increased Teaching Effectiveness. The disadvantages are: 1) Lecturers and students are afraid that their work will be completely replaced by AI, 2) Security and privacy, 3) Plagiarism, 4) Students are lazier than before because the features offered make it very easy for students to complete their college work, 5) Artificial Intelligence (AI) does not have Common Sense, 6) AI is able to learn and work on its own like humans, but AI cannot understand the purpose of the information it was made, 7) Student literacy will decrease because they don't have to bother reading journals or books, and 8) Using AI for a long time can have the potential to become addictive.

Nepali Virtual AI Assistant

With the development of Artificial Intelligence (AI), much of our regular redundant work that does not require critical thinking is replaced by machines. The scope of the Internet of Things (IoT) is also increasing due to AI. IoT acts as a suitable platform for the application of AI in real life. In this paper, we present a Virtual AI Assistant that combines AI with IoT. Our system uses an ESP8266 (Node MCU) chip as the core microcontroller for the home automation application. It is an IoT-compatible microcontroller with a cloud connectivity feature that is accomplished with the help of WiFi (Wireless fidelity). This allows home appliances such as lights, fans, rice cookers, etc. to be controlled by desktop or mobile applications from any part of the world. Our system uses speech recognition as its primary source for controlling the application. The speech recognition feature can detect and distinguish people’s voices which is a handy feature in terms of security and privacy. Other than IoT, software features such as note-making, application initiators, web navigation, and automatic typing through speech recognition are also implemented in this system. Our work also makes use of the Nepali language for IoT applications for switching home appliances on and off. The AI model was trained from scratch manually for the Nepali language as proper audio datasets for the Nepali language are not available. Our work in the development of Nepali Virtual AI Assistant is to integrate hardware and software with AI to create user-friendly automation and ease in people’s daily lives.

A comprehensive review of model compression techniques in machine learning

This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and data-intensive, the demand for computational resources and memory has surged accordingly. This escalation presents significant challenges for the deployment of artificial intelligence (AI) systems in real-world applications, particularly where hardware capabilities are limited. Therefore, model compression techniques are not merely advantageous but essential for ensuring that these models can be utilized across various domains, maintaining high performance without prohibitive resource requirements. Furthermore, this review underscores the importance of model compression in sustainable artificial intelligence (AI) development. The introduction of hybrid methods, which combine multiple compression techniques, promises to deliver superior performance and efficiency. Additionally, the development of intelligent frameworks capable of selecting the most appropriate compression strategy based on specific application needs is crucial for advancing the field. The practical examples and engineering applications discussed demonstrate the real-world impact of these techniques. By optimizing the balance between model complexity and computational efficiency, model compression ensures that the advancements in AI technology remain sustainable and widely applicable. This comprehensive review thus contributes to the academic discourse and guides innovative solutions for efficient and responsible machine learning practices, paving the way for future advancements in the field.Graphical abstract

Ironmaking process under artificial intelligence technology: A review

The ironmaking process is a complex and continuous operation, which makes it difficult to collect and predict the production parameters. To address this challenge, artificial intelligence (AI) deep learning has emerged as a promising solution. The paper discusses the evolution and utilisation of AI in the metallurgical industry. The paper emphasises the implementation of AI in various ironmaking processes, including raw material selection and charging for iron production, molten iron composition prediction in blast furnace (BF), and internal operational state and fault detection in BF. Additionally, the paper predicts BF gas and explores the utilisation of automated equipment such as cooling systems. Drawing upon existing literature, this paper emphasises that deep learning has numerous advantages in the ironmaking industry, including its ability to process data quickly, strong adaptability, and high accuracy. The paper also highlights several challenges that the future development of AI in the ironmaking field may encounter. Looking ahead, the future of deep learning in ironmaking appears promising.

Distributed and Collaborative Lightweight Edge Federated Learning for IoT Zombie Devices Detection

The fast development of artificial intelligence and Internet of Things (IoT) technologies has enabled various applications of smart cities, e.g., smart monitoring and surveillance. However, vulnerabilities of IoT devices bring new threats to the security of smart cities. To identify ubiquitous IoT botnet attacks, a distributed and collaborative lightweight edge federated learning model for IoT zombie devices detection is proposed, named FIOT. To reduce computational complexity and enhance the adaptability to new attack environment at the network edge, FIOT is designed in a lightweight manner based on feature dimensionality reduction and transfer learning. Three IoT botnet datasets are used to validate the effectiveness of the proposed FIOT. Experimental results show that FIOT has an accuracy loss of less than 3% in terms of F1 value compared to the centralized learning, but the training time of FIOT is only 14.3% of that of centralized learning. While ensuring high detection accuracy, the number of parameters of FIOT is compressed to 37.58% of the comparison method.

The sectoral aspect of staffing for strategic development of the sphere of artificial intelligence

The resource support of any developed strategy according to the chosen priorities is one of the most important stages of its formation and implementation. The solution of this task affects the dynamics of development of the economy’s high technology industries as the objects of strategizing in the emerging digital economy. One of the priority technological vectors of development of Russia is the sphere of artificial intelligence (AI), and so there arises the problem of the resource support of this direction. The purpose of the article is to study staffing as one of the key factors of strategic development of the Russian AI sphere in the industry context. The analysis is based on the methodology of strategizing by Professor V.L. Kvint and the concept of strategic human resources management by Doctor I.V. Novikova. The calculations are based on the Russian universities’ data on the number of graduates by educational programs in the AI sphere in 2023 and their employment. During the research the authors found out the number of graduates in the AI sphere by the industry specialization of educational programs; defined the sectoral specifics of employment of the graduates specializing in the AI sphere; identified the key employment organizations and jobs. The authors made conclusions about resource support of the AI development strategy in the industry context. The results will be useful for correcting the developed strategies in the AI sphere and for predicting staffing provision with the consideration of the time lag in the staff training.

High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.

Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for effective treatment. This study developed a high-performance artificial intelligence (AI) model to detect osteosarcoma from X-ray images using highly accurate annotated data to improve diagnostic accuracy at initial consultations. Traditional models trained on unannotated data have shown limited success, with sensitivities of approximately 60%-70%. In contrast, our model used a data-centric approach with annotations from an experienced oncologist, achieving a sensitivity of 95.52%, specificity of 96.21%, and an area under the curve of 0.989. The model was trained using 468 X-ray images from 31 osteosarcoma cases and 378 normal knee images with a strategy to maximize diversity in the training and validation sets. It was evaluated using an independent dataset of 268 osteosarcoma and 554 normal knee images to ensure generalizability. By applying the U-net architecture and advanced image processing techniques such as renormalization and affine transformations, our AI model outperforms existing models, reducing missed diagnoses and enhancing patient outcomes by facilitating earlier treatment. This study highlights the importance of high-quality training data and advocates a shift towards data-centric AI development in medical imaging. These insights can be extended to other rare cancers and diseases, underscoring the potential of AI in transforming diagnostic processes in oncology. The integration of this AI model into clinical workflows could support physicians in early osteosarcoma detection, thereby improving diagnostic accuracy and patient care.

Innovative Applications of Artificial Intelligence in Specific Domains

Artificial Intelligence (AI) is experiencing an unprecedented surge in development in today’s era, acting as a powerful wave sweeping across various industries and becoming the core force driving industry transformation. With its unique technological advantages and innovative capabilities, AI has shown remarkable potential in specific fields such as healthcare, education, and industry, opening up new and effective ways to solve long-standing practical problems and significantly enhance efficiency and quality. This paper will closely integrate the rich practical experience of Chongqing Soft River Turing AI Technology Co., Ltd. in relevant fields, and delve into the specific application scenarios, implementation methods, and actual effects of AI in these fields. Through the analysis of specific cases and data support, it reveals how AI can accurately address industry pain points, optimize business processes, improve production efficiency, and enhance service quality, as well as how it can bring extensive and positive impacts on society. We firmly believe that in-depth research and summary of these practical experiences can provide valuable references and insights for the further development of AI in various fields, promoting AI technology to better serve society and create a better future for humanity. At the same time, we also hope to showcase Chongqing Soft River Turing AI Technology Co., Ltd.’s innovative capabilities and leading position in the field of AI, to win more attention and support for the company’s development.

Artificial Intelligence in Advance Angiogenesis and Inflammation Research: A Breakthrough in Disease Prediction and Therapy

Background: Angiogenesis and inflammation are fundamental biological processes, crucial to human health. Dysregulation of these processes is implicated in diseases such as cancer, cardiovascular disorders, and autoimmune diseases. Despite advances in research, the complexity of these interactions has been challenging to understand. Recent developments in artificial intelligence (AI) offer a promising approach for overcoming these challenges, especially in big data analysis. This study explores AI applications in quantifying angiogenesis and inflammatory markers and predicting disease progression. Methods: AI algorithms, including machine learning (ML) and deep learning (DL), were employed to analyze high-throughput biological data. The study applied Lasso regression for biomarker discovery, Long Short-Term Memory (LSTM) networks for predicting disease progression, and Gaussian Mixture Models (GMM) for patient subgroup identification. Image analysis using DeepLabv3+ was conducted to assess angiogenesis and inflammatory markers in histological images. Model performance was evaluated using R-squared (R²), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics. Results: The AI framework demonstrated high accuracy in predicting disease progression, with notable R² values and low MSE and RMSE values. The application of AI led to the successful identification of angiogenesis-related genes and biomarkers in various diseases, including diabetic foot ulcers and chronic obstructive pulmonary disease. AI-based image analysis also provided precise quantification of angiogenesis and inflammation, enhancing the understanding of disease mechanisms. Conclusion: AI-driven approaches significantly improve the analysis of complex biological processes, offering new insights into angiogenesis and inflammation. The high predictive accuracy of the AI models underscores their potential in clinical applications, such as personalized treatment strategies and disease monitoring. As AI continues to evolve, its integration into biomedical research will likely yield further advancements in disease prediction, diagnosis, and treatment.

Network pharmacology for traditional Chinese medicine in era of artificial intelligence

AbstractTraditional Chinese Medicine Network Pharmacology (TCM-NP) is an interdisciplinary discipline that integrates information science, systems biology, network science and pharmacology, providing a systematic research methodology for TCM studies. With the development of artificial intelligence (AI) and multi-omics technologies, TCM-NP has entered a new era and can incorporate multimodal and high-dimensional data in the context of big data to enhance both theoretical foundations and technical capabilities. Despite its advancement, TCM-NP still faces challenges, particularly in ensuring the quality of data and research, as well as achieving more profound scientific discoveries. The field needs further innovation to obtain more precise and biomedically meaningful results. Overall research progress in TCM-NP depends on developing more accurate algorithms together with utilizing higher-quality and larger-scale data. This paper gives a perspective on the trends and characteristics of TCM-NP development and application in the era of AI.

Knowledge transfer, not just true beliefs: virtue epistemology as a philosophical basis for education

Introduction. Modern pedagogical practice is being reformed due to systemic changes in the ways of social interaction. Knowledge transfer is being significantly modified under the influence of technical innovations, although it remains traditionally one of the main tasks of the educational process. The development of artificial intelligence, neural networks and other analytical computer systems shows that propositional knowledge is not the privilege of the human mind. This state of affairs changes the idea of knowledge as such. In this regard, the participants of the educational process face the task of revising the philosophical basis of modern learning in terms of finding a new definition of knowledge. It is also necessary to develop new methodological recommendations that would correspond to modern epistemological theories of knowledge, belief and understanding. Materials and research methods. The article uses the methods of conceptual analysis and comparative study characteristic of philosophy. The research materials are sources on theory of knowledge, education and also on applied epistemology. Results. Virtue epistemology is one of the most actively developing theories of knowledge in contemporary philosophy and humanities. It is based on the idea that knowledge cannot be an accidental belief, but must be the merit of a cognitive agent. This approach solves many problems of the philosophical theory of knowledge and creates a subjectoriented view of the nature of the cognitive process. This attitude allows us to use virtue epistemology in debates about applied issues, including those related to the explanation of the tasks and content of education. Discussion. A feature of the analysis within virtue epistemology is attention to cognitive skills (reliabilism) and sustained intellectual dispositions (responsibilism) in their relationship to the process of formation of true beliefs. Virtue epistemology defines knowledge through the notion of merit in the manifestation of subjective qualities, so the student's motivation and the context of belief formation should be taken into account when developing specific methodological recommendations. Such an approach explains why standardized testing cannot serve as the sole and primary means of assessing learners. At the same time, the general semantic peculiarity of the notions of virtue allows us to appeal to examples of individuals as ideal types of certain qualities (this is called exemplarism). At the same time, it is necessary to explain why these traits are the excellences that make their bearer better in epistemic terms. Conclusion. In explaining the knowledge transfer, pedagogy can be based on the philosophical principles of virtue epistemology. Such integration enriches both disciplines: pedagogy gets a conceptual scheme adequate to the modern social and technological situation, and epistemology gets a practical application.

AI-Enabled Medical Assessment and Assistance for Vocal Disorders: A Comparative Study

Vocal disorders present significant challenges for patients and clinicians, impacting communication and quality of life. The development of artificial intelligence (AI) technologies offers promising possibilities for improving the assessment and management of vocal disorders. This study aims to evaluate the effectiveness and applicability of different AI approaches in this field through a comparative study of AI-enabled medical assessment and assistance for vocal disorders. Various AI techniques, including machine learning algorithms, deep learning models, and natural language processing methods, are explored in the context of diagnosing vocal disorders, planning treatments, and managing patients. The insights gained from this comparative study contribute to understanding the role of AI in transforming healthcare delivery for vocal disorders, highlighting opportunities, challenges, and future directions for utilizing AI to enhance medical assessment and assistance in this specialized field.

Mapping the Landscape of VFX and AI: A PRISMA-guided Systematic Review

Objective: This systematic literature review aims to comprehensively synthesize existing evidence on Visual Effects (VFX) and Artificial Intelligence (AI) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The primary objective is to map the landscape of VFX and AI, examining the historical development of VFX techniques and practices in the context of AI advancements. Methods: Following PRISMA guidelines, a thorough search of five electronic databases—IEEE Xplore, Scopus, Taylor & Francis Online, ACM Digital Library, and ScienceDirect—was conducted to identify articles published up to April 18, 2024. Inclusion criteria encompassed English-language journal articles. Independent reviewers screened articles for eligibility, resolving discrepancies through consensus. Data extraction and synthesis were performed systematically. Results: A total of 41 studies met the inclusion criteria and were included in the review. These studies covered a wide range of topics related to VFX and AI, with various study designs. The findings are presented in a narrative synthesis. Discussion: The review indicates that AI development in VFX is comprehensive, covering topics such as image processing, inpainting, video processing, 3D modelling, rendering, deepfake technology, style transfer, tracking, animation, simulation, rigging, texture compression, and depth map enhancement. These advancements highlight the transformative role of AI in enhancing creative expression, production efficiency, and the quality of visual effects. Conclusion: This systematic literature review provides a comprehensive overview of the current state of knowledge on AI and VFX. The integration of AI into VFX is profoundly transforming the industry, enhancing creative expression, production efficiency, and the quality of visual effects. Continued advancements in AI technologies promise to further revolutionize VFX practices, offering new possibilities and efficiencies for future productions.

Fairness in crowdwork: Making the human AI supply chain more humane

The vast quantities of data required to build artificial intelligence (AI) technologies are often annotated and processed manually, making human labor a critical component of the AI supply chain. These workers are sourced through digital labor (“crowdwork”) platforms that often are unregulated and offer low wages, raising concerns about labor standards in AI development. Using the results of a survey, this article aims to shed light on the experiences and perceptions of fair treatment among workers in the AI supply chain. The study reveals significant variability in workers’ experiences, identifies potential drivers of fairness, and highlights how design choices by labor platforms can significantly affect worker welfare. Drawing on lessons from physical supply chains, the article offers practical guidance to managers on how to enhance worker welfare within the AI supply chain and ensure that AI technologies are responsibly sourced.