Deep Learning Techniques for Image Recognition and Object Detection

Particularly in the fields of object identification and picture recognition, deep learning approaches have transformed the science of computer vision. This abstract provides a summary of recent developments and cutting-edge methods in deep learning for applications like object identification and picture recognition. The automated identification and classification of objects or patterns inside digital photographs is known as image recognition. Convolutional neural networks (CNNs), for example, have displayed outstanding performance in image identification tests. By directly learning hierarchical representations of visual characteristics from raw pixel data, these algorithms are able to recognize complex patterns and provide precise predictions. The ability for models to learn sophisticated visual representations straight from raw pixel data has transformed applications like object identification and picture recognition. The development of extremely accurate and effective systems has been accelerated by advances in deep learning architectures and large-scale annotated datasets. Further advances in object identification and picture recognition are anticipated as deep learning develops, with applications in a variety of fields including autonomous driving, surveillance, and medical imaging

Health – related quality of life of Kuwaiti women with breast cancer: a comparative study using the EORTC Quality of Life Questionnaire

<p>Abstract</p> <p>Background</p> <p>The Kuwaiti perspective on quality of life (QOL) in breast cancer is important because it adds the contribution from a country where the disease affects women at a relatively younger age and seems to be more aggressive. We used the EORTC QLQ – C30 and its breast-specific module (BR-23) to highlight the health-related QOL of Kuwaiti women with breast cancer, in comparison with the international data, and assessed the socio-demographic and clinical variables that predict the five functional scales and global QOL (GQOL) scale of the QLQ – C30.</p> <p>Methods</p> <p>Participants were consecutive clinic attendees for chemotherapy, in stable condition, at the Kuwait Cancer Control Center.</p> <p>Results</p> <p>The 348 participants were aged 20–81 years (mean 48.3, SD 10.3); 58.7% had stages III and IV disease. Although the mean scores for QLQ – C30 (GQOL, 45.3; and five functional scales, 52.6%–61.2%) indicated that the patients had poor to average functioning, only 5.8% to 11.2% had scores that met the </= 33% criterion for problematic functioning, while 12.0% to 40.0% met the >66% criterion for more severe symptoms. Most (47.8%–70.1%) met the >66% criterion for "good functioning" on the BR-23 functional scales. The mean scores of the QLQ – C30 indicated that, despite institutional supports, Kuwaiti women had clinically significantly poorer global QOL and functional scale scores, and more intense symptom experience, in comparison with the international data (i.e., </= 10% difference between groups). For the BR-23, Kuwaiti women seemed to have clinically significantly better functional scale scores, but more severe symptoms, especially systemic side effects and breast symptoms. Younger women had poorer HRQOL scores. In regression analysis, social functioning accounted for the highest proportion of variance for GQOL.</p> <p>Conclusion</p> <p>The relatively high number that met the criterion for good functioning on the functional scales is an evidence base to boost national health education about psychosocial prognosis in cancer. In view of the poor performance on the symptom scales, clinicians treating Kuwaiti women with breast cancer should prepare them for the acute toxicities of treatment and address fatigue. The findings call for the institution of a psycho-oncology service to address psycho-social issues.</p

Transnational mobilities of the tallest building: origins, mobilization and urban effects of Dubai’s Burj Khalifa

The media and scholarly descriptions and understandings of the tallest building in the world, namely the Burj Khalifa in Dubai, generally, have been simplified. Either celebrating or condemning it, these explanations typically stress the unique technological solutions, the symbolic and political motivations or the financial risk and economic gamble. This manuscript documents the origins–in terms of both its generation as centrepiece of the large-scale development project called Downtown Dubai and the mobilization of antecedents of Dubai’s icon (including the Kuala Lumpur City Centre, the Samsung Tower Palace Three, Seoul). Drawing on secondary data and prior research materials, the paper analyses the mobilities of architectural, engineering and real estate experts and solutions, arguing that this urban spectacle worked at multiple scales, that multiple actors embraced it for different purposes: the government celebrating the nation and the city, the developer gaining a distinct landmark in a massive development to market it internationally, enticing partners and regulators in subsequent transnational operations and the design experts testing unprecedented technological solutions. The conclusions concentrate on the diverse motivations behind this architectural piece and the importance of a place-based yet critical and multiscalar understanding of similar urban transformation processes and their uneven urban effects