Self-Supervised WiFi-Based Identity Recognition in Multi-User Smart Environments DOI Creative Commons
Hamada Rizk, Ahmed Elmogy

Sensors, Год журнала: 2025, Номер 25(10), С. 3108 - 3108

Опубликована: Май 14, 2025

The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective capturing spatial contextual information, often face challenges related to high costs, privacy concerns, susceptibility environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven identification system that leverages WiFi-based wireless sensing contrastive learning techniques. IdentiFi utilizes self-supervised semi-supervised extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even dynamic, multi-occupant settings. system's temporal contrasting modules enhance its ability model motion reduce multi-user interference, class-aware minimizes extensive labeled datasets. Extensive evaluations demonstrate outperforms existing methods terms scalability, adaptability, preservation, making it highly suitable homes, healthcare facilities, security systems, personalized services.

Язык: Английский

Enhancing Elderly Care Services through Integrated Sentiment Analysis and Knowledge Reasoning: A Deep Learning Approach DOI Creative Commons

Yongguan Ai,

Shiwei Chu, Juan Wang

и другие.

International Journal of Cognitive Computing in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

SE-ResNeXt-50-CNN: A Deep Learning Model for Lung Cancer Classification DOI
Annu Priya,

P. Shyamala Bharathi

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112696 - 112696

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Self-Supervised Multiple-Hierarchical Transformer for Abnormal Human Action Recognition in Uav Surveillance System DOI

Sumaya Abdulrhman Altuwairqi,

Salma Kammoun Jarraya

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Intelligent Sorting of Pecan Shelled Products Using Hyperspectral Fingerprints and Deep Learning DOI
Ebenezer O. Olaniyi, Christopher Kucha, Priyanka Dahiya

и другие.

Journal of Food Engineering, Год журнала: 2025, Номер unknown, С. 112533 - 112533

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Human crime activity recognition and shooting weapon detection in video frames using the contour approximation algorithm, and FastDTW classifier DOI Creative Commons

Y. V. K. Durga Bhavani,

Veerappa B. Pagi

Cogent Social Sciences, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 21, 2025

Язык: Английский

Процитировано

0

The NovelHAD algorithm to detect presence of human activity in videos based on MediaPipe pose and human landmarks DOI Creative Commons

Y. V. K. Durga Bhavani,

Veerappa B. Pagi

Cogent Social Sciences, Год журнала: 2025, Номер 11(1)

Опубликована: Март 10, 2025

Язык: Английский

Процитировано

0

Dynamic Agricultural Pest Classification Using Enhanced SAO-CNN and Swarm Intelligence Optimization for UAVs DOI Creative Commons
Shiwei Chu,

Wenxia Bao

International Journal of Cognitive Computing in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Convolutional Attention‐Based Bidirectional Recurrent Neural Network for Human Action Recognition DOI
Aditya Mahamkali, Manvitha Gali, Soumya Ranjan Jena

и другие.

Computational Intelligence, Год журнала: 2025, Номер 41(2)

Опубликована: Апрель 1, 2025

ABSTRACT Human activity recognition (HAR) technology plays a major role in today's world and is used detecting human actions poses real‐time. In the past, researchers employed statistical machine learning methods to build extract attributes of various movements manually. However, typical techniques are becoming increasingly ineffective face exponentially increasing waveform data that lacks unambiguous principles. With advancement deep technology, manual feature extraction no longer required, performance on challenging problems can be improved. models have such as time consumption, inaccuracy, vanishing gradient problem. Therefore, solve these problems, proposed study convolutional attention‐based bidirectional recurrent neural network detect activities provided samples. The input images first pre‐processed using an adaptive bilateral filtering approach improve their quality remove image noise. Then, crucial features recovered (CNN) based encoder‐decoder model. Finally, identify activities. model recognizes with higher effectiveness lower latency. behaviors identified HMDB51 dataset. acquired highest accuracy 95.46%, which 10.51% superior multi‐layer perceptron (MLP), 6.99% CNN, 12.76% long short‐term memory (LSTM), 5.59% Bidirectional LSTM (BiLSTM), 4.82% CNN‐LSTM, respectively.

Язык: Английский

Процитировано

0

Human Activity Recognition Using Hybrid CNN-RNN Architecture DOI Open Access
Ajith Muralidharan, Sazia Mahfuz

Procedia Computer Science, Год журнала: 2025, Номер 257, С. 336 - 343

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Spatio temporal 3D skeleton kinematic joint point classification model for human activity recognition DOI

S. Karthika,

Y. Nancy Jane,

H. Khanna Nehemiah

и другие.

Journal of Visual Communication and Image Representation, Год журнала: 2025, Номер unknown, С. 104471 - 104471

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0