Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review

Energies, Journal Year: 2023, Volume and Issue: 16(6), P. 2636 - 2636

Published: March 10, 2023

The normal development of “smart buildings,” which calls for integrating sensors, rich data, and artificial intelligence (AI) simulation models, promises to usher in a new era architectural concepts. AI models can improve home functions users’ comfort significantly cut energy consumption through better control, increased reliability, automation. This article highlights the potential using design functionality smart houses, especially implementing living spaces. case study provides examples how be embedded homes user experience optimize efficiency. Next, will explore thoroughly analyze thorough analysis current research on use technology variety innovative ideas, including interior Smart Building System Framework based digital twins (DT). Finally, explores advantages homes, emphasizing Through study, theme seeks provide ideas effectively functionality, convenience, overarching goal is harness by transforming we live our improving quality life. concludes discussing unresolved issues future areas usage houses. Incorporating into benefits homeowners, providing excellent safety convenience

Language: Английский

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Drone Video Anomaly Detection by Future Segmentation Prediction and Spatio-Temporal Relational Modelling

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 22395 - 22406

Published: Jan. 1, 2025

Language: Английский

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UAV sensor data applications with deep neural networks: A comprehensive survey

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 123, P. 106476 - 106476

Published: May 25, 2023

Language: Английский

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A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 114680 - 114713

Published: Jan. 1, 2023

Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They deployed places such as markets, hospitals, schools, banks, shopping malls, offices, smart cities. VSSs generate massive amount surveillance data, significant research has been published on the use machine learning algorithms to handle data. In this paper, we present an extensive overview thorough analysis cutting-edge methods VSSs. Existing surveys approaches video have some drawbacks, lack in-depth algorithms, omission certain methodologies, insufficient critical evaluation, absence recent algorithms. To fill these gaps, survey provides examination most for anomaly detection. A assessment their strengths, weaknesses, applicability well tailored classifications types different domains provided. Our study also offers insights into future development techniques VSS, positioning itself valuable resource both researchers practitioners field. Finally, share our thoughts what learned how it can help with new developments future.

Language: Английский

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Anomaly detection in cropland monitoring using multiple view vision transformer

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 23, 2025

Language: Английский

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Energy consumption auditing based on a generative adversarial network for anomaly detection of robotic manipulators

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 149, P. 376 - 389

Published: Aug. 2, 2023

Language: Английский

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A Cross-Modal Semantic Alignment and Feature Fusion Method for Bionic Drone and Bird Recognition

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3121 - 3121

Published: Aug. 23, 2024

With the continuous progress in drone and materials technology, numerous bionic drones have been developed employed various fields. These are designed to mimic shape of birds, seamlessly blending into natural environment reducing likelihood detection. However, such a high degree similarity also poses significant challenges accurately distinguishing between real birds drones. Existing methods attempt recognize both using optical images, but visual often results poor recognition accuracy. To alleviate this problem, paper, we propose cross-modal semantic alignment feature fusion (CSAFF) network improve accuracy CSAFF aims introduce motion behavior information as an auxiliary cue discriminability. Specifically, module (SAM) was explore consistent data provide more cues for birds. Then, (FFM) fully integrate information, which effectively enhances representability these features. Extensive experiments were performed on datasets containing experimental consistently show effectiveness proposed method identifying

Language: Английский

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Applications of GANs to Aid Target Detection in SAR Operations: A Systematic Literature Review

Drones, Journal Year: 2024, Volume and Issue: 8(9), P. 448 - 448

Published: Aug. 31, 2024

Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness enhancement of security flexibility in operations. However, a significant challenge arises from the quality sensors, terrain variability, noise, sizes targets images videos taken by them. Generative adversarial networks (GANs), introduced Ian Goodfellow, among their variations, can offer excellent solutions improving regarding super-resolution, noise removal, other image processing issues. To identify new insights guidance how apply GANs detect living beings SAR operations, PRISMA-oriented systematic literature review was conducted analyze primary studies that explore usage edge or object detection captured drones. The results demonstrate utilization GAN algorithms realm detection, along with metrics employed tool validation. These findings provide modify them aid target identification during stages.

Language: Английский

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A Machine Learning Approach for Knee Injury Detection from Magnetic Resonance Imaging

International Journal of Environmental Research and Public Health, Journal Year: 2023, Volume and Issue: 20(12), P. 6059 - 6059

Published: June 6, 2023

The knee is an essential part of our body, and identifying its injuries crucial since it can significantly affect quality life. To date, the preferred way evaluating through magnetic resonance imaging (MRI), which effective technique that accurately identifies injuries. issue with this method high amount detail comes MRIs challenging to interpret time consuming for radiologists analyze. becomes even more concerning when are required analyze a significant number in short period. For purpose, automated tools may become helpful assisting them evaluation these images. Machine learning methods, being able extract meaningful information from data, such as images or any other type promising modeling complex patterns MRI relating interpretation. In study, using real-life protocol, machine-learning model based on convolutional neural networks used detecting medial meniscus tears, bone marrow edema, general abnormalities exams presented. Furthermore, model's effectiveness terms accuracy, sensitivity, specificity evaluated. Based explored models reach maximum accuracy 83.7%, sensitivity 82.2%, 87.99% tears. 81.3%, 93.3%, 78.6% reached. Finally, abnormalities, 90.0% 84.2% specificity, respectively.

Language: Английский

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Assisting Visually Impaired People Using Deep Learning-based Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems on Remote Sensing Images

Deleted Journal, Journal Year: 2023, Volume and Issue: 2(2)

Published: Aug. 1, 2023

Anomaly detection in pedestrian walkways of visually impaired people (VIP) is a vital research area that utilizes remote sensing and aids to optimize traffic improve flow. Researchers engineers can formulate effective tools methods with the power machine learning (ML) computer vision (CV) identifying anomalies (i.e. vehicles) mitigate potential safety hazards walkways. With recent advancements ML deep (DL) areas, authors have found image recognition problem ought be devised as two-class classification problem. Therefore, this manuscript presents new sine cosine algorithm learning-based anomaly (SCADL-ADPW) algorithm. The proposed SCADL-ADPW technique identifies presence on images. techniques focus identification anomalies, i.e. vehicles VIP. To accomplish this, uses VGG-16 model for feature vector generation. In addition, SCA approach designed optimal hyperparameter tuning process. For detection, long short-term memory (LSTM) method exploited. experimental results are studied UCSD dataset. comparative outcomes stated improved technique.

Language: Английский

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