Supportive activities in oncological wards during the COVID-19 pandemic: a qualitative study

Introduction. The oncology ward is a challenging and unique workplace due to physical and psychological stress that staff experience and the need for their support. Cancer patients and oncology nurses have many needs, and support is one of the basic ones. This study aimed to explore supportive activities in the oncology ward during the COVID-19 pandemic. Material and methods. This qualitative study was conducted in Eastern and Southeastern Iran in 2020 and 2021 through a conventional content analysis approach. The participants included 21 (10 oncology nurses, 5 managers, and 6 cancer patients), who were selected through purposive sampling. To collect data, in-depth semi-structured face-to-face interviews were done. Interviews were continued until data saturation was achieved. After transcribing the interviews, the data were analyzed according to the steps proposed by Graneheim & Lundman. Results. The results consisted of three main themes and nine categories, namely the perceive of threat in sup-portive atmosphere in the oncology ward (cancer patients' sense of desperation and need for support, difficulty of working in the department, close relationships governing the ward), Seeking support in the oncology ward (Professional support, patient advocacy), and supportive divergence (poor family support, perceived poor social support, unsupportive behaviors, Being far from the supportive standards of working in an oncology ward). Conclusions. The results of the study have shown that the supportive activities in the oncology ward during the COVID-19 pandemic are affected by various factors. The experiences of participants provide new insight into supportive activities around managing oncology wards supportive needs during such stressful times.

Analysis of the level of physical activity of university faculty members during the coronavirus pandemic

The purpose of this study was to investigate the level of physical activity of faculty members at Allameh Tabatabai University during the coronavirus pandemic. This applied research has been done by a descriptive method. The statistical population of the study was the Allameh Tabatabai University faculty members in Iran. 284 people were selected through the available sampling method. A valid and reliable researcher-developed questionnaire was used for data collection, and a T-test was conducted for data analysis. The results of this study showed that the faculty members of the university do not have intense and moderate activities during the COVID-19 pandemic. But the general knowledge about the benefits of exercising during the coronavirus period has increased. In a general conclusion, COVID -19 crisis has created inactivity among university faculty members. Therefore, it is necessary to design and implement plans to increase the physical activity level among faculty members.

Masked Face Detection Using Artificial Intelligent Techniques

Nowadays, wearing a face mask is a vital routine in life, but threats are increasing in public due to the advantage of wearing face masks. Existing works do not perfectly detect the human face and also not possible to apply for different faces detection. To overwhelm this issue, in this paper we proposed real-time face mask detection. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, and face mask detection. In the first step, a set of keyframes are selected using the histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (parameterized CLAHE), and pose normalization (Angular Affine Transformation). In the third step, facial parts are segmented using the clustering approach i.e., Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, CapsNet based Feature Extraction is performed using CapsNet approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. Experiments conducted using Python IDLE 3.8 for the proposed model and also previous works as GMM with Deep learning (GMM + DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Video surveillance framework based on real-time face mask detection and recognition

In this paper we proposed a real-time face mask detection and recognition for CCTV surveillance camera videos. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, face mask detection, and face recognition. In the first step, a set of keyframes are selected using histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (CLAHE), and poses normalization (Angular Affine Transformation). In third step, facial parts are segmented using clustering approach i.e. Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, Pixel-based Feature Extraction is performed using Yolo Nano approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. The final step is face recognition which is implemented by a Kernel-based Extreme Learning Machine with Slime Mould Optimization (SMO). Experiments conducted using Python IDLE 3.8 for the proposed Yolo Nano model and also previous works as GMM with Deep learning (GMM+DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics. © 2021 IEEE.