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Author: search.filters.author.Kumar, A

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Now showing 1 - 3 of 3
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    PublicationOpen Access
    Unleashing the Power of Wireless Communication in Healthcare by Empowering Patient Care and Connectivity: A Comprehensive Survey
    (Institute of Electrical and Electronics Engineers, 2025-06-10) Chaudhary, U; Furqan A. M; Kumar, A; Sharma, A; Nalin J. D.K
    The emergence of the wireless network as a potentially revolutionary innovation has the ability to change the field of medical diagnostics. This in-depth study aims to explore the various aspects of using the latest wireless technologies to improve the standard of care given to patients and the interactions between patients and healthcare providers. This study investigates a wide range of issues such as patient-centric communication technology, 6G based applications using smart technologies, real-time communication protocols, implementation of artificial intelligence (AI) and blockchain technology in healthcare and the use of wireless devices for remote patient monitoring. 6G wireless communication brings transformative capabilities to healthcare, offering ultra-reliable and low-latency communication (URLLC), improved network capacity, and higher data rates. These advances enable the real-time transmission of critical health data, support complex medical applications, facilitate remote consultations, surgical robotics, and AI-driven diagnostics. This study highlights the significance and implications of combining these concepts in the context of 5G and beyond, paving the way for connected healthcare, personalized medicine, and unprecedented levels of efficiency and innovation. In addition, it also investigates the obstacles and potential associated with the implementation of wireless communication in the healthcare industry. These challenges and opportunities include data security and privacy issues, as well as the need for a robust communication infrastructure.
  • Thumbnail Image
    PublicationOpen Access
    Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model
    (Nature Research, 2025-03-17) Kumar, A; Bhattacharjee, S; Kumar, Ambrish; Jayakody, D. N.K
    Facial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.
  • Thumbnail Image
    PublicationOpen Access
    Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model
    (www.nature.com, 2025-03-17) Kumar, A; Bhattacharjee, S; Kumar, A; Jayakody, D. N. K
    Facial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.