The importance of detecting and preventing ovarian cancer is of utmost significance for women’s overall health and wellness. Referred to as the “silent killer,” ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.

Traffic accidents continue to be a significant cause of fatalities, injuries, and considerable disruptions on our highways. Understanding the underlying factors behind these incidents is crucial for improving safety on road networks. While recent studies have highlighted the usefulness of predictive modeling in uncovering factors leading to accidents, there remains a gap in explaining the inner workings of complex machine learning and deep learning models and how various features influence accident prediction. This lack of transparency may lead to these models being perceived as black boxes, potentially undermining trust in their findings among stakeholders. The primary aim of this research is to develop predictive models using diverse transfer learning techniques and shed light on the most influential factors using Shapley values. In predicting injury severity in accidents, we employ Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNET), EfficientNetB4, InceptionV3, Extreme Inception (Xception), Visual Geometry Group (VGG19), AlexNet, and MobileNet. Among these models, MobileNet emerges with the highest accuracy at 0.9817. Furthermore, by comprehending how different features impact accident prediction models, researchers can deepen their understanding of the factors contributing to accidents and devise more effective interventions for their prevention.

Since the Arab Spring, there has been a global interest in the use of technology in the Arab world. In particular, there is a growing interest in social media and its impact on democratic practices. The region came to the forefront once more with the refugee crisis as many of these studies address Arabs as users of Western technologies. Our experience working/researching HCI motivates us to assert that the current research on Arab HCI mostly deals with this population in a superficial way and is yet to reflect the diverse socio-political and economic facets of the region. This meeting aims to bring together HCI researchers and practitioners from the Arab World with those who are conducting/interested in research in this context. The goal is to start a dialog that leverages our \"insider\" understanding of HCI research in the Arab context to explore the challenges and unique opportunities for future research.

Since the Arab Spring, there has been a global growing interest in the use of technology in the Arab world. In particular, social media and its impact on democratic practices. The region came to the forefront once more with the refugee crisis with many of those studies address Arabs as users of technologies designed and developed in Western countries. Our experience working/researching HCI motivates us to assert that current research on Arab HCI mostly deals with this population in a superficial way and is yet to reflect the depth and richness of the Arab culture as well as the diversity of the socio-political and economic aspects of the region. This one-day workshop aims to bring together HCI researchers/practitioners from the Arab World with those who are conducting/interested in research in this context. The goal is to start a dialog that leverages our \"insider\" understanding of HCI research in the Arab context to explore challenges and unique opportunities for future research.