TAQWA: Teaching Adolescents Quality Wadhu/Ablution contactlessly using deep learning

This research presents a unique and innovative approach to teaching young children the proper steps of ablution (wazoo/wudu) by utilizing a non-invasive sensing system integrated with deep learning algorithms. However, most existing ablution detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. We conducted experiments with a group of youngsters to evaluate the system’s effectiveness, demonstrating its potential in fostering a deeper appreciation and comprehension of religious practices among young learners. This innovative privacy-preserving ablution system employs state-of-the-art UWB-radar technology with advanced Deep Learning (DL) techniques to effectively address the challenges mentioned above. The core focus of this system is to categorize the four fundamental ablution steps: Wash Face 3x, Wash Hand 3x, Wash Head 1x, and Wash Feet 3x. By transforming the collected data into spectrograms and harnessing the sophisticated DL models VGG16 and VGG19, the proposed system accurately detects these ablution steps, achieving an impressive maximum accuracy of 97.92% across all categories with the utilization of VGG16

Towards AI-assisted RF hearing aids

The development of effective hearing-assistive devices is essential as the prevalence of deafness grows with an ageing population. Where can AI support speech understanding? A team from the University of Glasgow discusses how lip‑reading hearing aids could be the future

Mirror, mirror on the wall: automating dental smile analysis with AI in smart mirrors

This paper presents a smart diagnostic framework for dental smile analysis. To accurately and efficiently identify esthetic issues from a single image of a smile, a convolutional neural network (CNN) was trained. To overcome the limitations of scarce data, a diffusion model was employed to generate dental smile images in addition to manually curated data. The CNN was trained and evaluated on three datasets: all real images, all generated images, and a hybrid dataset of equal proportions of real-to-generated images. All three models demonstrate accuracy significantly above the baseline in detecting excessive gingival display, unlocking a novel diagnostic method in smile analysis. Notably, the hybrid model achieved the highest accuracy of 81.61% (p value < 0.01), highlighting the effectiveness of generative data augmentation for machine learning. The proposed solution could be part of a standalone home-deployed smart mirror or connected to a network of an innovative Internet-of-Mirrors to facilitate patient-dentist communication

TAQWA: Teaching Adolescents Quality Wadhu/Ablution Contactlessly Using Deep Learning

This research presents a unique and innovative approach to teaching young children the proper steps of ablution (wazoo/wudu) by utilizing a non-invasive sensing system integrated with deep learning algorithms. However, most existing ablution detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. We conducted experiments with a group of youngsters to evaluate the system’s effectiveness, demonstrating its potential in fostering a deeper appreciation and comprehension of religious practices among young learners. This innovative privacy-preserving ablution system employs state-of-the-art UWB-radar technology with advanced Deep Learning (DL) techniques to effectively address the challenges mentioned above. The core focus of this system is to categorize the four fundamental ablution steps: Wash Face 3x, Wash Hand 3x, Wash Head 1x, and Wash Feet 3x. By transforming the collected data into spectrograms and harnessing the sophisticated DL models VGG16 and VGG19, the proposed system accurately detects these ablution steps, achieving an impressive maximum accuracy of 97.92% across all categories with the utilization of VGG16

Cx43 Expression Correlates with Breast Cancer Metastasis in MDA-MB-231 Cells In Vitro, In a Mouse Xenograft Model and in Human Breast Cancer Tissues

Connexins regulate multiple cellular functions and are considered tumor suppressors. Connexin43 (Cx43) is frequently down-regulated in breast tumors. However, Cx43 regulation during cancer onset and metastasis is complex and context-dependent. We investigated the effect of Cx43 over-expression or knock-down on the metastatic potential of MDA-MB-231 breast cancer cells in vitro and in vivo and in human breast cancer tissues. MDA-MB-231 cells over-expressing (Cx43D) or down-regulating Cx43 (shCx43) were generated and used in proliferation, migration, and invasion assays. The regulation of genes/proteins implicated in progression, invasion and metastasis was assessed in vitro and in immune-compromized mice injected with MDA-MB-231, Cx43D or shCx43 cells. Primary tumor onset/growth, metastasis and overall survival of these animals was monitored and evaluated. In addition, Cx43 expression in human breast carcinoma samples was assessed by qPCR. Cx43 over-expression increased protein levels of epithelial markers E-cadherin and zonula occludens 1 expression and resulted in the sequestration of β-catenin at the cell membrane, while Cx43 knock-down induced protein expression of the mesenchymal marker N-cadherin and an increased invasive potential of shCx43 cells. In vivo, in mice xenografted with breast cancer cells, Cx43 over-expression decreased tumor volume, attenuated cell metastasis to lungs and liver and increased overall mice survival. Importantly, the expression of Cx43 in triple negative human breast cancer tissues is also down-regulated. Collectively, Cx43 over-expression induced an epithelial-like phenotype in MDA-MB-231 cells and suppressed tumor growth and metastasis to secondary organs in vivo. In contrast, Cx43 knock-down in MDA-MB-231 cells induced a mesenchymal phenotype with increased cell invasion leading to an enhanced metastatic phenotype. These data provide evidence for a pivotal role of Cx43 in breast cancer metastasis and support the potential targeting of connexins in breast cancer therapy