DReLNet: abnormal human activity classification in theft surveillance video using reinforcement learning based fusion

Signal Image and Video Processing, Год журнала: 2025, Номер 19(3)

Опубликована: Янв. 17, 2025

Язык: Английский

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Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Sensors, Год журнала: 2023, Номер 23(13), С. 6040 - 6040

Опубликована: Июнь 29, 2023

Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor smart living services applications, from energy management healthcare transportation, is efficacy human action recognition (HAR). HAR, rooted computer vision, seeks identify actions activities using visual data various sensor modalities. This paper extensively reviews literature on HAR amalgamating key contributions challenges while providing insights into future research directions. The review delves essential aspects state art potential societal implications this technology. Moreover, meticulously examines primary application sectors that stand gain such as homes, healthcare, cities. By underscoring significance four dimensions context awareness, availability, personalization, privacy offers a comprehensive resource researchers practitioners striving advance applications. methodology involved conducting targeted Scopus queries ensure coverage relevant publications field. Efforts have been made thoroughly evaluate existing literature, gaps, propose comparative advantages lie its addressing limitations previous offering valuable

Язык: Английский

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Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy

Journal of Hazardous Materials, Год журнала: 2024, Номер 471, С. 134309 - 134309

Опубликована: Апрель 16, 2024

Язык: Английский

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Review on Human Action Recognition in Smart Living: Multimodality, Real-time Processing, Interoperability, Resource-Constrained Processing, and Sensing Technology

Опубликована: Май 3, 2023

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies homes and cities to enhance the quality of life for citizens. Sensing human action recognition are crucial aspects this concept. living applications span various domains, such as energy consumption, healthcare, transportation, education, which greatly benefit from effective recognition. This field, originating computer vision, seeks recognize actions activities using not only visual data but also many other sensor modalities. paper comprehensively reviews literature on smart environments, synthesizing main contributions, challenges, future research directions. review selects five key domains: Technology, Multimodality, Real-time Processing, Interoperability, Resource-Constrained they encompass critical required successfully deploying living. These domains highlight essential role sensing play developing implementing solutions. serves valuable resource researchers practitioners seeking explore further advance field

Язык: Английский

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A multi-resolution fusion approach for human activity recognition from video data in tiny edge devices

Information Fusion, Год журнала: 2023, Номер 100, С. 101953 - 101953

Опубликована: Июль 29, 2023

Язык: Английский

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Situation identification in smart wearable computing systems based on machine learning and Context Space Theory

Information Fusion, Год журнала: 2023, Номер 104, С. 102197 - 102197

Опубликована: Дек. 16, 2023

Wearable devices and smart sensors are increasingly adopted to monitor the behaviors of human artificial agents. Many applications rely on capability such recognize daily life activities performed by monitored users in order tailor their with respect occurring situations. Despite constant evolution sensing technologies numerous research this field, an accurate recognition in-the-wild situations still represents open challenge. This work proposes a novel approach for situation identification capable recognizing which they occur different environments behavioral contexts, processing data acquired wearable environmental sensors. An architecture situation-aware computing system is proposed, inspired Endsley's situation-awareness model, consisting two-step identification. The first identifies via learning-based technique. Simultaneously, context recognized using Context Space Theory. Finally, fusion between state allows identifying complex user acting. knowledge regarding forms basis smarter can be realized. has been evaluated ExtraSensory public dataset compared state-of-the-art techniques, achieving accuracy 96% significantly low computational time, demonstrating efficacy approach.

Язык: Английский

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Federated Learning via Augmented Knowledge Distillation for Heterogenous Deep Human Activity Recognition Systems

Sensors, Год журнала: 2022, Номер 23(1), С. 6 - 6

Опубликована: Дек. 20, 2022

Deep learning-based Human Activity Recognition (HAR) systems received a lot of interest for health monitoring and activity tracking on wearable devices. The availability large representative datasets is often requirement training accurate deep learning models. To keep private data users' devices while utilizing them to train models huge datasets, Federated Learning (FL) was introduced as an inherently distributed paradigm. However, standard FL (FedAvg) lacks the capability heterogeneous model architectures. In this paper, we propose via Augmented Knowledge Distillation (FedAKD) FedAKD evaluated two HAR datasets: A waist-mounted tabular dataset wrist-mounted time-series dataset. more flexible than federated it enables collaborative with various capacities. considered experiments, communication overhead under 200X less compared methods that communicate models' gradients/weights. Relative other model-agnostic methods, results show boosts performance gains clients by up 20 percent. Furthermore, shown be relatively robust statistical scenarios.

Язык: Английский

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Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Опубликована: Июнь 9, 2023

Smart Living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor Living services applications, from energy management healthcare transportation, is efficacy human action recognition (HAR). HAR, rooted computer vision, seeks identify actions activities using visual data various sensor modalities. This paper extensively reviews literature on HAR amalgamating key contributions challenges while providing insights into future research directions. The review delves essential aspects state art potential societal implications this technology. Moreover, meticulously examines primary application sectors that stand gain such as smart homes, healthcare, cities. By underscoring significance four dimensions Context Awareness, Data Availability, Personalization, Privacy serves a valuable resource researchers practitioners striving advance applications.

Язык: Английский

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Multi-Modal Hate Speech Recognition Through Machine Learning

Опубликована: Янв. 8, 2024

Because of the speedy surge in social media's expansion, proliferation malicious and harmful poses a substantial worry contemporary society. The identification hate speech on platforms like Twitter is crucial for various tasks such as controversial event extraction, AI chatterbot creation, content suggestions, sentiment analysis. Researchers have invested considerable effort addressing challenging task identifying hostile due to rise information. objective classify tweets Hateful, Offensive, or neither. However, this highly complex intricate nature natural language constructs, encompassing different manifestations animosity directed at demographics, multitude ways same meaning can be expressed.Previous research has predominantly relied manual feature extraction employed representation-learning techniques followed by linear classifiers. Nevertheless, deep learning methods recently demonstrated significant accuracy improvements problems across speech, vision, text applications. In study, This paper present an idea automatic classifications inappropriate expressions hostility using transfer models. research, leverage classified tweet datasets obtained from Kaggle conduct experiments. Findings reveal that multilingual-BERT model its pre-trained version deliver superior outcomes. Specifically, BERT notably improves classification hateful up 92% when compared other algorithms.

Язык: Английский

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A Review of Recent Techniques for Human Activity Recognition: Multimodality, Reinforcement Learning, and Language Models

Algorithms, Год журнала: 2024, Номер 17(10), С. 434 - 434

Опубликована: Сен. 28, 2024

Human Activity Recognition (HAR) is a rapidly evolving field with the potential to revolutionise how we monitor and understand human behaviour. This survey paper provides comprehensive overview of state-of-the-art in HAR, specifically focusing on recent techniques such as multimodal techniques, Deep Reinforcement Learning large language models. It explores diverse range activities sensor technologies employed for data collection. then reviews novel algorithms used emphasis multimodality, gives an datasets physiological data. also delves into applications HAR healthcare. Additionally, discusses challenges future directions this exciting field, highlighting need continued research development fully realise various real-world applications.

Язык: Английский

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