Criminal Social Work To Overcome Overcapacity In Post-Pandemic Prisons

There are currently various problems with prisons, including the emergence of problems regarding Over Capacity in prisons. Especially in the era of the Covid-19 pandemic, where prisons in Indonesia are not comparable to limited capacity space. In the renewal of criminal law, in this case, the Draft Law on the Criminal Code (RUU-KUHP) has discussed various alternative crimes, one of which is the existence of social work crimes. Research on social work crimes to overcome overcapacity in Post Pandemic Prisons (Prisons) uses normative legal research methods. The legal material used is to use library studies (Library Research). The regulations regarding social work crimes have been implemented in several countries such as the Netherlands, Portugal and Denmark. Social work crimes are also known as short-term deprivation of liberty and as an alternative attempt to carry out certain crimes with a short time. © 2023 M. Musa, Elsi Elvina and Evi Yanti.

WE-Net: An Ensemble Deep Learning Model for Covid-19 Detection in Chest X-ray Images Using Segmentation and Classification

Amidst the increasing surge of Covid-19 infections worldwide, chest X-ray (CXR) imaging data have been found incredibly helpful for the fast screening of COVID-19 patients. This has been particularly helpful in resolving the overcapacity situation in the urgent care center and emergency department. An accurate Covid-19 detection algorithm can further aid this effort to reduce the disease burden. As part of this study, we put forward WE-Net, an ensemble deep learning (DL) framework for detecting pulmonary manifestations of COVID-19 from CXRs. We incorporated lung segmentation using U-Net to identify the thoracic Region of Interest (RoI), which was further utilized to train DL models to learn from relevant features. ImageNet based pre-trained DL models were fine-tuned, trained, and evaluated on the publicly available CXR collections. Ensemble methods like stacked generalization, voting, averaging, and the weighted average were used to combine predictions from best-performing models. The purpose of incorporating ensemble techniques is to overcome some of the challenges, such as generalization errors encountered due to noise and training on a small number of data sets. Experimental evaluations concluded on significant improvement in performance using the deep fusion neural network, i.e., the WE-Net model, which led to 99.02% accuracy and 0.989 area under the curve (AUC) in detecting COVID-19 from CXRs. The combined use of image segmentation, pre-trained DL models, and ensemble learning (EL) boosted the prediction results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.