The lungs are two of the most crucial organs in the human body because they are connected to the respiratory and circulatory systems. Lung cancer, COVID-19, pneumonia, and other severe diseases are just a few of the many threats. The patient is subjected to an X-ray examination to evaluate the health of their lungs. A radiologist must interpret the X-ray results. The rapid advancement of technology today can help people in many different ways. One use of deep learning in the health industry is in the detection of diseases, which can decrease the amount of money, time, and energy needed while increasing effectiveness and efficiency. There are other methods that can be used, but in this research, the convolutional neural network (CNN) method is only used with three architectures, namely ResNet-50, ResNet-101, and ResNet-152, to aid radiologists in identifying lung diseases in patients. The 21,885 images that make up the dataset for this study are split into four groups: COVID-19, pneumonia, lung opacity, and normal. The three algorithms have fairly high evaluation scores per the experiment results. F1 scores of 91%, 93%, and 94% are assigned to the ResNet-50, ResNet-101, and ResNet-152 architectures, respectively. Therefore, it is advised to use the ResNet-152 architecture, which has better performance values than the other two designs in this study, to categorize lung diseases experienced by patients.

Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO‐CNN and segment it with the FCN‐UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%.

Digital disparities remain a significant challenge in Indonesia, particularly across its diverse regions, with uneven access to digital infrastructure, skills, and economic opportunities. This study aims to map these digital disparities at the district level, analyze the spatial distribution and clustering of digital transformation using the Digital Society Index of Indonesia (IMDI), and investigate the key drivers of digital inequality across four core pillars: Infrastructure and Ecosystem, Digital Skills, Empowerment, and Jobs. To measure the IMDI, primary data were collected from the industrial sector and the general population over three years (2022–2024), combined with secondary data on internet usage and service standards. A multistage random sampling approach ensured representativeness, considering demographic variations and industrial segments. The analysis employed spatiotemporal methods to capture temporal trends and spatial clustering. The results revealed a significant IMDI increase from 37.80 in 2022 to 43.18 in 2023, followed by stability at 43.34 in 2024. The hotspots of digital transformation remain concentrated on Java Island, while low spots persist in eastern Indonesia. This study provides critical insights into Indonesia’s digital readiness, identifying priority areas for targeted interventions to bridge the digital divide and foster equitable digital development.

In the era of big data and ubiquitous internet connectivity, user feedback data plays a crucial role in product development and improvement. However, extracting valuable insights from the vast pool of unstructured text data found in user feedback presents significant challenges. In this paper, we propose an innovative approach to tackle this challenge by combining the Contextualized Topic Model (CTM) and the Masked and Permuted Pre-training for Language Understanding (MPNet) model. Our approach aims to create a more accurate and context-aware topic model that enhances the understanding of user experiences and opinions. To achieve this, we first search for the optimal number of topics, focusing on generating distinguishable, general, and unique topics. Next, we perform hyperparameter optimization to fine-tune the model and maximize coherence metrics. The result is an exceptionally effective model that outperforms established topic modeling methods, including LSI, NMF, LDA, HDP, NeuralLDA, ProdLDA, ETM, and the default CTM, achieving the highest coherence CV score of 0.7091. In this study, the combination of CTM and MPNet has proven highly effective in the context of user feedback topic modeling. This model excels in generating coherent, distinguishable, and highly relevant user feedback topics, capturing the nuanced nature of user feedback data. The topics generated from this model include ’Music and Audio Streaming,’ ’Application Performance,’ ’Banking, Financial Services, and Customer Support,’ ’User Experience,’ ’Other Topics,’ ’Application Content,’ and ’Application Features.’ Our contributions include a powerful tool for developers to gain deeper insights, prioritize actions, and enhance user satisfaction by incorporating feedback into future product iterations. Furthermore, we introduce a new dataset as an open-source resource for further exploration and validation of user feedback analysis techniques and general natural language processing applications. With our proposed approach, we strive to drive business success, improve user experiences, and inform data-driven decision-making processes, ultimately benefiting both developers and users alike.

COVID-19 is the disease that has spread over the world since December 2019. This disease has a negative impact on individuals, governments, and even the global economy, which has caused the WHO to declare COVID-19 as a PHEIC (Public Health Emergency of International Concern). Until now, there has been no medicine that can completely cure COVID-19. Therefore, to prevent the spread and reduce the negative impact of COVID-19, an accurate and fast test is needed. The use of chest radiography imaging technology, such as CXR and CT-scan, plays a significant role in the diagnosis of COVID-19. In this study, CT-scan segmentation will be carried out using the 3D version of the most recommended segmentation algorithm for bio-medical images, namely 3D UNet, and three other architectures from the 3D UNet modifications, namely 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. These four architectures will be used in two cases of segmentation: binary-class segmentation, where each architecture will segment the lung area from a CT scan; and multi-class segmentation, where each architecture will segment the lung and infection area from a CT scan. Before entering the model, the dataset is preprocessed first by applying a minmax scaler to scale the pixel value to a range of zero to one, and the CLAHE method is also applied to eliminate intensity in homogeneity and noise from the data. Of the four models tested in this study, surprisingly, the original 3D UNet produced the most satisfactory results compared to the other three architectures, although it requires more iterations to obtain the maximum results. For the binary-class segmentation case, 3D UNet produced IoU scores, Dice scores, and accuracy of 94.32%, 97.05%, and 99.37%, respectively. For the case of multi-class segmentation, 3D UNet produced IoU scores, Dice scores, and accuracy of 81.58%, 88.61%, and 98.78%, respectively. The use of 3D segmentation architecture will be very helpful for medical personnel because, apart from helping the process of diagnosing someone with COVID-19, they can also find out the severity of the disease through 3D infection projections.

Lung diseases have a significant impact on respiratory health, causing various symptoms and posing challenges in diagnosis and treatment. This research presents a methodology for classifying lung diseases using chest X-ray images, specifically focusing on COVID-19, pneumonia, and normal cases. The study introduces an optimal architecture for convolutional neural network (CNN) and long short-term memory (LSTM) models, considering evaluation metrics and training efficiency. Furthermore, the issue of imbalanced datasets is addressed through the application of some image augmentation techniques to enhance model performance. The most effective model comprises five convolutional blocks, two LSTM layers, and no augmentation, achieving an impressive F1 score of 0.9887 with a training duration of 91 s per epoch. Misclassifications primarily occurred in normal cases, accounting for only 3.05% of COVID-19 data. The pneumonia class demonstrated excellent precision, while the normal class exhibited high recall and an F1 score. Comparatively, the CNN-LSTM model outperformed the CNN model in accurately classifying chest X-ray images and identifying infected lungs. This research provides valuable insights for improving lung disease diagnosis, enabling timely and accurate identification of lung diseases, and ultimately enhancing patients’ outcomes.