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Статті в журналах з теми "AI analysis"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Jastržemskis, Rihards, and Pēteris Grabusts. "ANALYSIS OF SYMBOLIC AI AND CONNECTIONIST AI APPROACHES." HUMAN. ENVIRONMENT. TECHNOLOGIES. Proceedings of the Students International Scientific and Practical Conference, no. 28 (September 10, 2024): 44–47. http://dx.doi.org/10.17770/het2024.28.8257.

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Анотація:

This research project aims to conduct a comparative analysis of two prominent paradigms within artificial intelligence: Symbolic AI (also known as classical AI or GOFAI - Good Old-Fashioned AI) and Connectionist AI (also known as neural network-based AI). Both approaches have played significant roles in the development of AI systems, but they differ in their underlying principles, methodologies, and applications. By comparing and contrasting these two approaches, this study seeks to provide insights into their strengths, weaknesses, and suitability for different types of AI tasks.

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2

H. R, Sindhu Rashmi, Prof Anisha B. S, and Dr Ramakanth Kumar. P. "Smart Document Analysis Using AI-ML." International Journal of Innovative Research in Computer Science & Technology 7, no. 3 (2019): 54–70. http://dx.doi.org/10.21276/ijircst.2019.7.3.6.

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3

R Leach, A., D. P Dolata, and C. K Prout. "AI in conformational analysis." Journal of Molecular Graphics 5, no. 2 (1987): 107. http://dx.doi.org/10.1016/0263-7855(87)80011-5.

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4

Lahat, Rotem, Noa Berick, Majd Hajouj, Tali Teitelbaum, and Isaac Shochat. "AIAIAI: AI insights on amassing influence in AI-related publications – an AI-assisted retrospective analysis into AI-related publication." BMJ Health & Care Informatics 32, no. 1 (2025): e101244. https://doi.org/10.1136/bmjhci-2024-101244.

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ObjectivesThis study analyses the trend of artificial intelligence (AI)-related publications in the medical field over the past decade and demonstrates the potential of AI in automating data analysis. We hypothesise exponential growth in AI-related publications, with continuous growth in the foreseeable future.MethodsRetrospective, AI-assisted analysis was conducted using the OpenAI application programming interface for data collection and evaluation. Publications from the top 50 medical journals (Web of Science, Journal Citation Report, 2022) covering 2014 to June 2024. A total of 315 209 pap

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5

LeCun, Yann. "Artificial Intelligence in Scientific Research: Transforming Data Analysis and Discovery." International Journal of Innovative Computer Science and IT Research 1, no. 01 (2025): 1–9. https://doi.org/10.63665/ijicsitr.v1i01.01.

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Анотація:

Artificial Intelligence (AI) has become a transformative tool in scientific research, reshaping traditional methodologies by enabling advanced data analysis, hypothesis testing, and predictive modeling. The integration of machine learning (ML), deep learning (DL), and natural language processing (NLP) has significantly accelerated discoveries in medicine, physics, chemistry, environmental science, and other disciplines. AI-driven technologies allow researchers to process large datasets, identify complex patterns, and generate predictive insights with unprecedented accuracy and speed. These inn

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6

Ha, Ju-Hye, Sung-Ji Kim, and Chang-Hoon Oh. "Impact of AI Literacy on AI Ethics: A Scenario-Based Analysis." Journal of Korea Multimedia Society 28, no. 2 (2025): 333–52. https://doi.org/10.9717/kmms.2025.28.2.333.

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7

Alok, Kumar. "AI-Driven Market Trend Analysis." Journal of Research and Innovation in Technology, Commerce and Management Volume 2, Issue 4 (2025): 2454–61. https://doi.org/10.5281/zenodo.15301748.

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Artificial Intelligence (AI) has changed market trend analysis through real-time scanning and accurate forecasting of trends. AI technologies, such as machine learning, natural language processing (NLP) and big data analytics, allow organizations to uncover trends, take actions and displace the constraints of traditional methods. This research studies AI-oriented methods, including sentiment analysis, deep learning and predictive modeling. It demonstrates the ability of AI to process significant amounts of data, the areas AI might be used and discusses limitations, such as data quality issues,

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8

HAWALAH, YOUSIF. "Will of AI-Mansour Bin Abi Amir AI-Amri AI-Andalusi: Study and Analysis." Journal of King Abdulaziz University-Educational Sciences 7, no. 1 (1994): 225–66. http://dx.doi.org/10.4197/edu.7-1.11.

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9

Ibadildin, Nurkhat, Zhaxat Kenzhin, Gaukhar Yeshenkulova, et al. "Artificial intelligence in project management: A bibliometric analysis." Problems and Perspectives in Management 23, no. 2 (2025): 252–64. https://doi.org/10.21511/ppm.23(2).2025.17.

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Анотація:

This study explores the integration of artificial intelligence (AI) into project management through a bibliometric analysis of scholarly publications and patents from 2001 to 2024. The paper aims to identify key trends, emerging applications, and global contributions to this evolving field. The study analyzes 51,752 patents from Lens, 5.5 million scholarly records from Google Scholar, and 436,380 records from Web of Science, providing a comprehensive overview of AI’s impact on project management. The analysis maps the evolution of AI-driven project management practices, focusing on resource al

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10

Mihail Mateev. "Comparative Analysis on Implementing Embeddings for Image Analysis." Journal of Information Systems Engineering and Management 10, no. 17s (2025): 89–102. https://doi.org/10.52783/jisem.v10i17s.2710.

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This research explores how artificial intelligence enhances construction maintenance and diagnostics, achieving 95% accuracy on a dataset of 10,000 cases. The findings highlight AI's potential to revolutionize predictive maintenance in the industry. The growing adoption of image embeddings has transformed visual data processing across AI applications. This study evaluates embedding implementations in major platforms, including Azure AI, OpenAI's GPT-4 Vision, and frameworks like Hugging Face, Replicate, and Eden AI. It assesses their scalability, accuracy, cost-effectiveness, and integration f

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11

Song, J. S. "M009 AI-based microbiome analysis." Clinica Chimica Acta 530 (May 2022): S4. http://dx.doi.org/10.1016/j.cca.2022.04.299.

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12

Prasad, Potu Ram, Mutyala Uday Kiran, and V. Subapriya. "AI Integrated Holistic Resume Analysis." IOSR Journal of Computer Engineering 27, no. 1 (2025): 01–06. https://doi.org/10.9790/0661-2701010106.

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Holistic resume analysis is a comprehensive evaluation process that examines a candidate's entire resume, rather than focusing solely on individual components such as education, experience, or skills. This approach integrates various elements to paint a complete picture of the candidate's professional identity and potential. By considering factors such as formatting, language, and personal branding alongside traditional qualifications, holistic analysis provides valuable insights into how effectively a resume communicates the candidate's value proposition to potential employers. It emphasizes

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13

Li, Dr Tian. "AI Image Analysis and Quantification." Journal of Medical Imaging and Radiation Sciences 55, no. 3 (2024): 101468. http://dx.doi.org/10.1016/j.jmir.2024.101468.

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14

Dolata, Daniel P., Andrew R. Leach, and Keith Prout. "WIZARD: AI in conformational analysis." Journal of Computer-Aided Molecular Design 1, no. 1 (1987): 73–85. http://dx.doi.org/10.1007/bf01680558.

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15

McMillan, John. "Generative AI and Ethical Analysis." American Journal of Bioethics 23, no. 10 (2023): 42–44. http://dx.doi.org/10.1080/15265161.2023.2249852.

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16

Chauhan, Shally, Vinny Sharma, and Indlacheruvu Siddhartha. "Fingerprints analysis using AI Algorithm." International Journal of Medical Toxicology & Legal Medicine 25, no. 3and4 (2022): 273–79. http://dx.doi.org/10.5958/0974-4614.2022.00092.4.

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17

Khatiwada, Bhuwan, Bishwa Prakash Subedi, Niraj Duwal, Rewan Gautam, and Surammya Sharma Dahal. "Cricket Shots Analysis Using AI." KEC Journal of Science and Engineering 7, no. 1 (2023): 22–32. http://dx.doi.org/10.3126/kjse.v7i1.60513.

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Анотація:

Cricket is often described as a contest between bat and ball, and batting is considered one of its prime disciplines. While batting a batter tries to hit the ball with a different variety of shots to guide the ball in any specific direction of their wish. The types of shots include cover drive, straight drive, pull shot, reverse sweep, and many more. It is tedious task to classify shots manually and provide the insights for the batter. Our goal is to classify the shots using Support Vector Machine (SVM) and to provide an analysis of how well someone plays a particular shot using image processi

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18

Kumar, Vishesh, Shep Bryan, and Steve Pressé. "Conversational AI-empowered biophysical analysis." Biophysical Journal 123, no. 3 (2024): 551a. http://dx.doi.org/10.1016/j.bpj.2023.11.3331.

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19

Mollaei, Parisa, and Amir Barati Farimani. "AI-enhanced protein conformational analysis." Biophysical Journal 123, no. 3 (2024): 338a. http://dx.doi.org/10.1016/j.bpj.2023.11.2056.

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20

Hall, John Storrs. "Self-improving AI: an Analysis." Minds and Machines 17, no. 3 (2007): 249–59. http://dx.doi.org/10.1007/s11023-007-9065-3.

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21

Perera, W. A. D., Mr Jeewaka Perera, and Mr. Tharaniyawarma.K. "AI Based Speech Analysis Framework." International Research Journal of Innovations in Engineering and Technology 08, no. 01 (2024): 94–104. http://dx.doi.org/10.47001/irjiet/2024.801013.

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The pioneering AI-Based Speech Analysis Framework presented in this research paper was painstakingly created to help people overcome linguistic obstacles, notably in the context of English language communication. Through a thorough speech analysis, the framework's multimodal approach enables real-time evaluation of emotional state, fluency, stress levels, and even identification recognition. This framework delivers a sophisticated and perceptive interpretation of spoken language by utilizing cutting-edge artificial intelligence approaches, hence promoting an enhanced and successful communicati

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22

Robinson, Connor J., Bruce Dickie, Claudia Lindner, et al. "Complex wound analysis using AI." Computers in Biology and Medicine 190 (May 2025): 109945. https://doi.org/10.1016/j.compbiomed.2025.109945.

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23

Prof. Jyoti Gaikwad, Aniket Manohare, Shweta Munde, Anwar Shaikh, and Diksha Subhedar. "AI-Based Exploratory Data Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3876–84. https://doi.org/10.32628/cseit25112860.

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Анотація:

In today's world, where data is being generated at an unprecedented rate, organizations often struggle to make sense of the vast and complex information they collect. Extracting valuable insights from such massive datasets has become a major challenge. Traditionally, Exploratory Data Analysis (EDA) has relied on statistical techniques and manual processes. While effective, these methods can be slow, tedious, and difficult to scale when dealing with big data. This paper explores how Artificial Intelligence (AI) is transforming the way we approach EDA. By integrating AI technologies, such as Mac

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24

Tondarkar, Abhishek A. "AI Driven Vulnerability Analysis Systems." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50022.

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Abstract— With the increasing frequency and sophistication of cyber threats, the need for intelligent and adaptive security solutions has become more critical than ever. Artificial Intelligence (AI) is playing a transformative role in cybersecurity by enhancing the detection of system vulnerabilities, anticipating potential threats, and enabling automated incident response. This research introduces VulneraX, an AI-driven vulnerability analysis system built using the Flask framework. The system is trained on data from the National Vulnerability Database (NVD) covering the years 2020 to 2023. Vu

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25

Jeong, Soo Young, and Chul Hyun Lee. "Analysis of AI education status and interests of elementary school teachers through online community analysis." Korean Association of Practical Arts Education 36, no. 1 (2023): 1–21. http://dx.doi.org/10.24062/kpae.2023.36.1.1.

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The purpose of this study was to explore the current status of elementary AI education, the interest shown by elementary teachers in AI education, and topics related to elementary AI education, to obtain basic data on the direction of AI education policy and ways to revitalize AI education. After deleting duplicate posts and posts unrelated to the research topic in the post data collected by IndiSchool, a representative online community for elementary school teachers was established, followed by conducting basic and text-mining analyses using 454 posts as the research subject. First, to

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26

Gupta, Adya. "The Impact of Artificial Intelligence on Teenagers/Adolescents: A Comprehensive Analysis." Advances in Machine Learning & Artificial Intelligence 6, no. 2 (2025): 01–03. https://doi.org/10.33140/amlai.06.02.02.

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This research paper explores the multifaceted effects of Artificial Intelligence (AI) on teenagers, encompassing cognitive development, social dynamics, and ethical considerations. I took an interdisciplinary approach to investigate how the integration of AI technologies into the daily lives of adolescents shapes their behaviors, relationships, and overall well-being. This research paper provides a comprehensive examination of the impact of AI on teenagers, recognizing the potential benefits and challenges associated with the integration of AI technologies into their lives. It emphasizes the i

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27

Kaustubh Kabtiyal, Manan Mistry,. "GenViz AI: Data Visualization using Generative AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45237.

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Data analysis plays a pivotal role in decision-making, but its complexity often makes it inaccessible to non-technical users. Traditional approaches require expertise in programming, statistics, and visualization tools, creating a barrier for many. This research introduces a system leveraging Artificial Intelligence (AI), Large Language Models (LLMs), and PandasAI to simplify data analysis. The system integrates Retrieval-Augmented Generation (RAG) techniques, allowing users to upload datasets (e.g., Excel files) and query them in natural language. With the ability to process, summarize, and v

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28

Baek, Daeun, WanSang Son, Ji Hoon Song, and MYUNGHYUN YOO. "Meta-analysis of learning effectiveness using generative AI." Korean Association for Educational Information and Media 30, no. 4 (2024): 1261–85. http://dx.doi.org/10.15833/kafeiam.30.4.1261.

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29

Wang, Xuquan, Tianyang Feng, Yujie Xing, et al. "Edge accelerated reconstruction using sensitivity analysis for single-lens computational imaging." Advanced Imaging 2, no. 3 (2025): 031001. https://doi.org/10.3788/ai.2025.10003.

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30

Sharipov, Rinat. "Analysis and Reduction of Errors in AI Models." American Journal of Engineering and Technology 07, no. 05 (2025): 202–10. https://doi.org/10.37547/tajet/volume07issue05-20.

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The issue of errors in artificial intelligence (AI) models is a critical aspect that requires systematic analysis and the application of effective methods for their reduction. Errors in AI models can occur at various stages of development and deployment, including data collection, model training, and operation phases. The key tasks in this field involve identifying error sources and applying approaches aimed at eliminating them. Methods such as cross-validation, regularization, and the use of ensemble models play a significant role in reducing errors and improving prediction accuracy. Therefor

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31

MORCOS, ZEINA, MICHEL ACHKAR, YISROEL GRABIE, SUDEEP ACHARYA, and DAVID ROTBLAT. "AI REVOLUTIONIZES PE DIAGNOSIS: A COMPARATIVE ANALYSIS OF TWO AI MODELS." CHEST 166, no. 4 (2024): A6362. http://dx.doi.org/10.1016/j.chest.2024.07.007.

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32

Vinay Kumar Ankusarao. "Learning from AI Failures: A Critical Analysis of Enterprise AI Implementation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1808–16. https://doi.org/10.32628/cseit251112176.

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This article presents a comprehensive analysis of an enterprise-scale artificial intelligence implementation failure at a leading service industry organization. The article examines a proof-of-concept project aimed at transforming customer success operations through AI-powered insights and recommendations. Through detailed examination of the technical architecture, implementation methodology, and failure points, this article identifies critical challenges in data quality, system integration, and scalability that led to the project's unsuccessful outcome. The analysis provides valuable insights

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33

Chuyen, Nguyen Thi Hong, and Nguyen The Vinh. "How teachers’ AI readiness affects AI integration: insights from modeling analysis." International Journal of Evaluation and Research in Education (IJERE) 14, no. 2 (2025): 1496. https://doi.org/10.11591/ijere.v14i2.28242.

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<span lang="EN-US">This study examined the dynamic relationships among professional development and training, resources and accessibility, attitudes and perceptions, pedagogical knowledge and expertise, teachers’ artificial intelligence (AI) readiness, and the integration of AI in teaching practices. The study employed a non-random purposive approach and utilized the structural equation modeling (SEM) method. Five hypotheses were meticulously formulated and subsequently subjected to empirical scrutiny, involving a cohort of 224 participants. Data were collected via Google Form and subjec

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34

Park, Sangsung, and Sunghae Jun. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis." Sustainability 12, no. 2 (2020): 505. http://dx.doi.org/10.3390/su12020505.

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At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends o

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35

Bae, Jinah, Junghun Lee, and Jungwon Cho. "Analysis of AI Ethical Competence to Computational Thinking." JOIV : International Journal on Informatics Visualization 6, no. 2-2 (2022): 506. http://dx.doi.org/10.30630/joiv.6.2-2.1126.

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Artificial Intelligence (AI) is a driving force leading the intelligent information society. Major advanced countries have established AI into key policy projects and made continuous efforts to nurture and develop future talents through AI education. Unlike conventional software, AI can infer results through training with data, and if there is a data bias, it may cause social and ethical problems. These problems incur extensive damage to society, so ethical consideration is essential in terms of effectiveness and efficiency in implementing AI. Computational thinking aims to perform effective a

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36

Basu, Nibedita, and Rhishikesh Dave. "Comparative Analysis of Laws in AI." Journal of Lifestyle and SDGs Review 5, no. 3 (2025): e05575. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n03.pe05575.

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Objectives: This article aims to examine the global efforts in regulating artificial intelligence (AI), highlighting the diverse approaches taken by different countries. It focuses on key regulatory parameters such as ethical concerns, privacy, security, and economic impact. Additionally, the study explores the legislative gap in India, where policy frameworks exist but no specific AI law has been enacted. Methods: The research adopts a qualitative approach, utilizing comparative analysis to assess AI regulations across various countries. The study evaluates how the European Union, the United

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37

Ouyang, Tinghui, AprilPyone MaungMaung, Koichi Konishi, Yoshiki Seo, and Isao Echizen. "Stability Analysis of ChatGPT-Based Sentiment Analysis in AI Quality Assurance." Electronics 13, no. 24 (2024): 5043. https://doi.org/10.3390/electronics13245043.

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Анотація:

In the era of large AI models, the intricate architectures and vast parameter sets of models such as large language models (LLMs) present significant challenges for effective AI quality management (AIQM). This paper investigates the quality assurance of a specific LLM-based AI product: ChatGPT-based sentiment analysis. The study focuses on stability issues, examining both the operation and robustness of ChatGPT’s underlying large-scale AI model. Through experimental analysis on benchmark datasets for sentiment analysis, the findings highlight the ChatGPT-based sentiment analysis’s susceptibili

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38

Praja, Chrisna Bagus Edhita, Hary Abdul Hakim, Basri, Yulia Kurniaty, and Elvira Purnama Sari. "Legal Analysis of AI-Generated Creations: Copyright Law Perspectives." E3S Web of Conferences 622 (2025): 03005. https://doi.org/10.1051/e3sconf/202562203005.

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Анотація:

The development of Artificial Intelligence (AI) in digital transformation has a significant impact on people's lives. AI is able to solve complex problems with high accuracy to produce a creation. This raises problems and negative impacts related to copyright. The absence of explicit rules governing the creation of AI leads to legal uncertainty. The 2014 copyright law does not fully cover AI-generated works. Legal analysis shows that AI is just a computer programming tool that performs tasks based on human commands. AI uses algorithms and computer training for recognition, prediction, and deci

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39

Gogin, Alexander A., Anna N. Fedorova, Ramil F. Vagapov, and Alexey V. Sergeev. "Austrian and Russian constitutionalism: comprehensive analysis." Revista Amazonia Investiga 10, no. 41 (2021): 121–30. http://dx.doi.org/10.34069/ai/2021.41.05.12.

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Анотація:

The article deals with some issues that characterize the Basic Laws of the Republic of Austria and the Russian Federation. When choosing the leading research method, the authors of this article were guided by the fundamental dialectical means of cognition, which is the ascent from the individual to the general. The comparison technique used in this case is based on a consistent understanding and analysis of similar conditions, rules and requirements for the Republic of Austria and the Russian Federation. The applied historical and legal method made it possible to consider various historical ev

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40

Aakash, Aluwala. "Integrating AI for Real-Time User Behaviour Analysis and Reporting." Journal of Scientific and Engineering Research, 7, no. 6 (2020): 285–92. https://doi.org/10.5281/zenodo.13337988.

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Анотація:

This paper focuses on how AI can be applied for real-time analysis of users’ behaviour and provides a report on the same. As these tools are intended to offer information from the user data, they take privacy and autonomy away if further protection is not in place. From the streaming behavioural data, AI allows the identification of user profiles and outliers without requiring human intervention. However, excessive control also causes privacy and non-transparency concerns, pre-conceptions or prejudice, and automatic decision-making. The secondary qualitative data is used to acquire the r

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41

Nan Wang, Nan Wang, Suqi Li Nan Wang, Chenhui Wang Suqi Li, and Li Zhao Chenhui Wang. "Current Status and Emerging Trends of Generative Artificial Intelligence Technology: A Bibliometric Analysis." 網際網路技術學刊 25, no. 3 (2024): 477–85. http://dx.doi.org/10.53106/160792642024052503013.

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Анотація:

<>With the widespread application of ChatGPT (Chat Generative Pre-trained Transformer), its superordinate concept, generative artificial intelligence (Gen AI), has received increasing attention from researchers. The current study attempted to explore the current status and emerging trends of Gen AI technology research by visualizing the relevant published articles using the Biblioshiny tool. A total of 1,902 academic articles in the Web of Science (WOS) database published between 2014-2022 were analyzed. Annual publications, most productive journals and countries, co-authors, co-occurrin

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Budiraharjo, Muhammad Husein, Alva Hendi Muhammad, and Kusnawi. "MSME AI Readiness Analysis Using The AIRI Framework." Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) 9, no. 2 (2024): 33–44. http://dx.doi.org/10.20527/jtiulm.v9i2.307.

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AI is expected to become one of the key technologies supporting the development of MSMEs, which represent a major pillar of Indonesia's economy. Successful adoption and implementation of AI require the right strategies, one of which stems from an analysis of a company’s AI readiness. In this study, an AI readiness analysis was conducted using the AIRI framework on six MSMEs from various business sectors. The results of the analysis provided the AI readiness levels of each MSME, along with comparisons to similar industries and to industries of comparable business scale (MSME). The analysis also

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Khamza, D., N. Amilbek, R. Abay, D. Son, and N. Taubakabyl. "ARTIFICIAL INTELLIGENCE AND HUMAN COLLABORATION: A PSYCHOLOGICAL ANALYSIS." Sciences of Europe, no. 129 (November 27, 2023): 138–41. https://doi.org/10.5281/zenodo.10209409.

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This article presents a psychological analysis of the collaboration between artificial intelligence (AI) and humans, focusing on the implications for human cognition, decision-making, and emotional responses. It explores the increasing integration of AI into daily life and its profound influence on the workplace, emphasizing the importance of trust in AI systems for successful human-AI interaction. The paper discusses the barriers to AI adoption, such as fear of job loss and skepticism, and proposes strategies to overcome these challenges through education, transparency, and ethical AI develop

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Sadikov, Hurshid. "Predictive Justice and Artificial Intelligence: Comparative Legal Analysis of Judicial Practice." American Journal of Political Science Law and Criminology 7, no. 6 (2025): 36–39. https://doi.org/10.37547/tajpslc/volume07issue06-07.

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The article is dedicated to research on the implementation of artificial intelligence (AI) technologies in the justice sector, with a focus on predictive coding and "predicted justice." A comparative legal analysis of the practice of the USA, China, European Union countries, and France was conducted. Special attention is paid to the role of AI at the proof stage: analysis of evidence, automation of legal procedures, and reduction of corruption risks. Examples of court cases illustrate both the possibilities and risks of applying AI in court proceedings. Institutional and ethical limitations, i

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Rad, Dana, Alina Felicia Roman, Daniel Mara, et al. "Exploring AI integration in education: a sequential mediation analysis." Journal of Educational Sciences & Psychology 15 (76), no. 2 (2024): 167–76. https://doi.org/10.51865/jesp.2024.2.15.

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This study explores the psychological impact of AI technology on students' recognition of educational benefits from AI integration, mediated by variation of the psychological influence of AI technology, benefits of rapid feedback provided by AI in the learning process, and perception of AI involvement in career and education. Conducted across six Romanian universities with 675 valid responses, it investigates a sequential mediation model linking AI technology's psychological influence, feedback benefits, career perceptions, and Benefits of AI integration in education. Results reveal a positive

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Yang, G. "Analysis of Ai Xuan Realist Paintings." Университетский научный журнал, no. 46 (2019): 210–13. http://dx.doi.org/10.25807/pbh.22225064.2019.46.210.213.

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Sharma*, Shreta, and Santosh Pandey. "Integrating AI Techniques in Requirements Analysis." International Journal of Innovative Technology and Exploring Engineering 9, no. 6 (2020): 582–89. http://dx.doi.org/10.35940/ijitee.e2826.049620.

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Анотація:

Requirements Analysis (RA) remains one of the most central processes in requirements engineering. It is a process of evaluating and discovering possible structures to create a contracted set of broad and reliable requirements. The major aim of requirements analysis is to produce a requirements specification document with great quality. The analysis of study exposes that experts have arranged major supports by developing various methods/tools/framework/techniques of requirement analysis process. Though, one of the major problems faced by developers is poor communication and frequently changes i

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Nashiroh, Putri Khoirin, and Ranu Iskandar. "Bibliometric analysis: Learning using generative AI." Journal of Research in Instructional 4, no. 1 (2024): 194–204. http://dx.doi.org/10.30862/jri.v4i1.392.

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Анотація:

This research aims to 1) analyze the number of articles per year and their types, 2) analyze the five articles with the most citations, 3) analyze keywords in the data collected, and 4) analyze the relationship between authors. Bibliometric analysis was used to help researchers study bibliographic content and citation analysis of each article taken from Harzing's Publish or Perish (PoP) 8 database. The data was taken from Scopus January 2018-2024 entitled, “learning using generative AI” in English. The maximum number of articles accessed is 500 articles. The collected articles are stored in CS

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Lansky, A. L., and A. G. Philpot. "AI-based planning for data analysis." IEEE Expert 9, no. 1 (1994): 21–27. http://dx.doi.org/10.1109/64.295136.

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Sheh, Raymond, and Isaac Monteath. "Defining Explainable AI for Requirements Analysis." KI - Künstliche Intelligenz 32, no. 4 (2018): 261–66. http://dx.doi.org/10.1007/s13218-018-0559-3.

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