Feature Dimensions of Artificial Intelligences Challenges and Techniques - A Survey DOI Open Access

S. Hemalatha,

Kiran Mayee Adavala,

Chandra Shekhar S N

и другие.

International Journal of Electronics and Communication Engineering, Год журнала: 2024, Номер 11(12), С. 107 - 122

Опубликована: Дек. 31, 2024

Artificial Intelligence (AI) is rapidly transforming sectors such as healthcare, education, and public services, contributing new solutions that advance efficiency, management, overall outcomes. However, despite its vast potential, AI adoption faces numerous challenges, including ethical concerns (e.g., algorithmic bias), data privacy issues, integration difficulties with legacy systems. This paper provides a comprehensive survey of applications across these sectors, analyzing over 60 recent studies from 2019 to 2024 after the PRISMA methodology. The study identifies key factors influencing successful implementation by highlighting sector-specific challenges shared barriers. framework was applied for systematic selection, inclusion exclusion criteria, screening, extraction, ensuring only relevant, high-quality were reviewed. These experimental results reveal models consistently outperform state-of-the-art techniques in critical domains, medical diagnosis, personalised service optimisation. hybrid approach, which combines Convolutional Neural Networks (CNNs) Recurrent (RNNs), outperforms existing addressing preprocessing, model architecture, hyperparameter Additionally, explores future up-and-coming technologies quantum computing, blockchain, metaverse while providing strategies overcome legal, cultural, infrastructural barriers adoption. findings offer actionable insights researchers, practitioners, policymakers, emphasising need both technical innovation considerations growth execution.

Язык: Английский

Cueing Flight Object Trajectory and Safety Prediction Based on SLAM Technology DOI Creative Commons
Chao Fan,

Weike Ding,

Kun Qian

и другие.

Journal of Theory and Practice of Engineering Science, Год журнала: 2024, Номер 4(05), С. 1 - 8

Опубликована: Май 14, 2024

With the rapid development of artificial intelligence and robot technology, SLAM as a key component, has been paid more attention. technology enables robots to autonomously navigate, build maps, achieve accurate positioning in unknown environments, providing strong support for autonomy unmanned vehicles. In this paper, position prediction method flying object based on application EvolveGCN model behavior are introduced. First, through fusion liDAR data, we can accurately predict movement trajectory objects, thereby improving safety efficiency system. Secondly, with model, able capture dynamic changes environment predictions objects. Through experimental verification, accuracy our significantly improved both simulation real environment, which indicates feasibility effectiveness practical application, provides an important reference technical autonomous navigation, aerial surveillance other fields.

Язык: Английский

Процитировано

17
Current State of Autonomous Driving Applications Based on Distributed Perception and Decision-Making DOI

Baoming Wang,

Han Lei,

Zuwei Shui

и другие.

Journal of improved oil and gas recovery technology., Год журнала: 2024, Номер 7(3), С. 15 - 22

Опубликована: Май 15, 2024

This article reviews the key role of distributed cloud architecture in autonomous driving systems and its integration with intelligent computing networks. By spreading resources across multiple geographic locations, enables localized processing storage data, reducing latency improving real-time decision making vehicles. The points out that combination technology network provides a powerful solution to meet challenges technology. dynamically allocating deeply integrating cloud, network, chip technologies, gives enhanced data capabilities ensure stable reliable performance variety scenarios. Finally, paper highlights synergy marks an important milestone for transportation systems, heralding accelerated adoption solutions automotive industry, pace innovation transformation.

Язык: Английский

Процитировано

17
AI Face Recognition and Processing Technology Based on GPU Computing DOI Creative Commons

Huixiang Li,

Ang Li, Yuning Liu

и другие.

Journal of Theory and Practice of Engineering Science, Год журнала: 2024, Номер 4(05), С. 9 - 16

Опубликована: Май 14, 2024

In recent years, with the development of deep neural network technology, real-time object detection has become increasingly common in mobile applications. However, practical application requirements drive algorithm to optimize terms speed, energy consumption and accuracy. This paper introduces artificial intelligence field face recognition, especially using TensorRT accelerated reasoning technology improve speed performance recognition. At same time, also discusses key role GPU computing expounds importance AI chips for optimizing inference tasks. Through analysis experimental results methods, advantages prospects BlazeFace applications are demonstrated, which provides a valuable reference industry.

Язык: Английский

Процитировано

13
Robot Navigation and Map Construction Based on SLAM Technology DOI
Zihan Li, Chao Fan,

Weike Ding

и другие.

Journal of improved oil and gas recovery technology., Год журнала: 2024, Номер 7(3), С. 8 - 14

Опубликована: Май 15, 2024

SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in the field of robotics, which realizes autonomous navigation robots unknown environments through real-time positioning, mapping path planning. This paper first introduces basic principle workflow technology, including sensor data fusion, state estimation map construction. Then, by comparing analyzing construction methods traditional raster visual advantages disadvantages different representations are shown. Finally, combined with practical application scenario, wide logistics, intelligent manufacturing other fields is discussed, its future development direction prospected.

Язык: Английский

Процитировано

13
Analysis of Financial Market using Generative Artificial Intelligence DOI Creative Commons
Yuning Liu, Junliang Wang

Academic Journal of Science and Technology, Год журнала: 2024, Номер 11(1), С. 21 - 25

Опубликована: Май 21, 2024

This paper delves into the utilization of Generative Artificial Intelligence (GAI) for virtual financial advising and analysis in capital markets. Initially, it outlines fundamental principles GAI its significance decision-making. Subsequently, scrutinizes shortcomings conventional advisory models through a review literature empirical data. It then examines emerging trends benefits intelligent advising, contrasting them with traditional models. Following this, elucidates practical applications generative AI finance, encompassing investment guidance, risk evaluation,

Язык: Английский

Процитировано

5
Machine Learning-Driven Digital Identity Verification for Fraud Prevention in Digital Payment Technologies DOI

Lichen Qin,

Yuqiang Zhong,

Wang Han

и другие.

Journal of improved oil and gas recovery technology., Год журнала: 2024, Номер 7(3), С. 1 - 7

Опубликована: Май 15, 2024

This article explores how machine learning techniques can be used to drive digital authentication prevent fraud in payment technologies. First, it introduces the development trend and risk of technology, then analyzes limitations traditional methods, focusing on potential authentication. It specific application scenarios authentication, including data collection preparation, feature engineering, model selection training, as well real-time monitoring anti-fraud processing. Finally, current challenges solutions are discussed, future technology. Through in-depth analysis these contents, aims provide readers with valuable insights help them better use technology improve security reliability payments promote sustainable economy.

Язык: Английский

Процитировано

5
Exploring User Behavioral Intentions and Their Relationship With AI Design Tools: A Future Outlook on Intelligent Design DOI Creative Commons
Ma Hui, N Li

IEEE Access, Год журнала: 2024, Номер 12, С. 149192 - 149205

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

5
The Application and challenges of virtual reality technology on modern visual communication design DOI Creative Commons

Jack Mao

Salud Ciencia y Tecnología - Serie de Conferencias, Год журнала: 2025, Номер 4, С. 1477 - 1477

Опубликована: Фев. 12, 2025

Introduction: Virtual reality (VR) technology is transforming visual communication design by providing immersive experiences that enhance the production and presentation of content. This study explores application VR in this field, emphasizing its transformative potential while addressing associated challenges. Methods: We utilized Golden Eagle Optimized Flexible Bayesian Neural Network (GEO-FBNN) to predict feature distributions, such as element position color, under specific conditions. A dataset comprising high-quality elements, real-time user interaction patterns, system data was pre-processed for consistency, through min-max normalization cleaning. Experiments were conducted using Python 3.8 on Windows 10, ensuring compatibility with hardware. Results: The implementation a deep learning approach significantly improved processing capabilities systems. It established connections between frames, revealing insights accuracy responsiveness. findings suggest effectively mitigates challenges integration, including motion sickness navigation ease. Conclusions: research provides comprehensive overview how can address obstacles incorporating design. results indicate not only reduction technical but also an increase creative opportunities, paving way more integrated future virtual environments.

Язык: Английский

Процитировано

0
The Role of Interaction Design Based on Fuzzy Decision Support System in Improving User Experience DOI
Jian Li, Bin Zhang

International Journal of Fuzzy Systems, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

0
Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions DOI Creative Commons
Muhammad Liaquat Raza,

Syed Belal Hassan,

Subia Jamil

и другие.

Frontiers in Neuroinformatics, Год журнала: 2025, Номер 19

Опубликована: Май 2, 2025

Introduction Alzheimer’s disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy predicting progression. Method This narrative review synthesizes current literature on applications using neuroimaging. The process involved comprehensive search of relevant databases (PubMed, Embase, Google Scholar ClinicalTrials.gov ), selection pertinent studies, critical analysis findings. We employed best-evidence approach, prioritizing high-quality studies identifying consistent patterns across the literature. Results Deep architectures, including convolutional neural networks, recurrent transformer-based models, have shown remarkable potential analyzing neuroimaging data. These models can effectively structural functional modalities, extracting features associated with pathology. Integration multiple modalities has demonstrated improved compared single-modality approaches. also promise predictive modeling, biomarkers forecasting Discussion While approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, limited generalizability diverse populations are significant hurdles. clinical translation these requires careful consideration interpretability, transparency, ethical implications. future AI neurodiagnostics looks promising, personalized treatment strategies.

Язык: Английский

Процитировано

0