Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison.

In this study, we propose a specimen tube prototype and smart specimen transport box using radio frequency identification (RFID) and narrow band–Internet of Things (NB-IoT) technology to use in the Department of Laboratory Medicine, King Chulalongkorn Memorial Hospital. Our proposed method replaces the existing system, based on barcode technology, with shortage usage and low reliability. In addition, tube-tagged barcode has not eliminated the lost or incorrect delivery issues in many laboratories. In this solution, the passive RFID tag is attached to the surface of the specimen tube and stores information such as patient records, required tests, and receiver laboratory location. This information can be written and read multiple times using an RFID device. While delivering the specimen tubes via our proposed smart specimen transport box from one clinical laboratory to another, the NB-IoT attached to the box monitors the temperature and humidity values inside the box and tracks the box’s GPS location to check whether the box arrives at the destination. The environmental condition inside the specimen transport box is sent to the cloud and can be monitored by doctors. The experimental results have proven the innovation of our solution and opened a new dimension for integrating RFID and IoT technologies into the specimen logistic system in the hospital.

Synchrophasor data from Phasor Measurement Unit (PMU) indicate the health of the electrical system. However, the PMU performance relies entirely on a supported communication network. The existing communication approaches do not guarantee the error-free channel for Phasor Measurement Unit network. Meanwhile, the mean or median approaches are commonly used to impute the missing value. These methods, however, fail to recover some valuable frequency events. In this study, we first proved the multiple linear regression features in many synchronized frequency data streams in the short-time window. Then, we proposed the Bagged Averaging of Multiple Linear Regression model which handles and fulfills the missing values in synchronized frequency data measurement fast and efficiently. This technique was based on the ensemble learning by bootstrapping and averaging many multiple linear regressions to predict the missing values. Various experiments on the synchronized frequency measurement data from Texas Synchrophasor Network have demonstrated the effectiveness of our proposed approach in recovering the missing frequency events. Our proposed approach guarantees the performance of real-time wide area monitoring system applications such as frequency analysis or stability monitoring and trending.

Age‐related macular degeneration (AMD) diagnosis using fundus images is one of the critical missions of the eye‐care screening program in many countries. Various proposed deep learning models have been studied for this research interest, which aim to achieve the mission and outperform human‐based approaches. However, research efforts are still required for the improvement of model classification accuracy, sensitivity, and specificity values. In this study, we proposed the model named as ViT‐AMD, which is based on the latest Vision Transformer (ViT) structure, to diagnosis a fundus image as normal, dry AMD, or wet AMD types. Unlike convolution neural network models, ViT consists of the attention map layers, which show more effective performance for image classification task. Our training process is based on the 5‐fold cross‐validation and transfer learning techniques using Chula‐AMD dataset at the Department of Ophthalmology, the King Chulalongkorn Memorial Hospital, Bangkok. Furthermore, we also test the performance of trained model using an independent image datasets. The results showed that for the 3‐classes AMD classification (normal vs. dry AMD vs. wet AMD) on the Chula‐AMD dataset, the averaged accuracy, precision, sensitivity, and specificity of our trained model are about 93.40%, 92.15%, 91.27%, and 96.57%, respectively. For result testing on independent datasets, the averaged accuracy, precision, sensitivity, and specificity of trained model are about 74, 20%, 75.35%, 74.13%, and 87.07%, respectively. Compared with the results from the baseline CNN‐based model (DenseNet201), the trained ViT‐AMD model has outperformed significantly. In conclusion, the ViT‐AMD model have proved their usefulness to assist the ophthalmologist to diagnosis the AMD disease.

The annual degradation rate (DR) of photovoltaics (PV) system is a critical factor to evaluate the energy performance and the levelized cost of electricity (LCOE) during its operation lifetime. However, the DR of a particular system strongly depends on the technical configuration such as PV module and array, inverter configuration, and also the climatic conditions. Therefore, a real operation dataset of DR is necessary to PV engineer in order to estimate energy performance and the LCOE for a particular system. This article presents the annual DR for a group of PV systems in Bangkok, Thailand which share the same monocrystalline silicon (Mono-Si) solar cell and inverter brand, over a four-year period. Instead of using simple linear regression, we apply the linear mixed effects method to estimate the DR value, which is suitable to formula a time-series data. The annual DR was found about 2.7% per year, with the 95% confidence interval from 0.7% to 4.6% per year. Hence, the operation lifetime of PV system until it reaches 80% of their initial energy conversion performance is about 7 years, with the 95% confidence interval from 4 years to 28 years. The resulting DR is informative and useful for further study on PV system performance and cost of investment in tropical region. Furthermore, we are the first group in Thailand to estimate the DR of PV station at system scale based on the mixed effects method. Finally, our study has enriched the knowledge about the operation of Mono-Si PV station in real operation condition.