BackgroundIndonesia's hospital‐based Severe Acute Respiratory Infection (SARI) surveillance system, Surveilans Infeksi Saluran Pernafasan Akut Berat Indonesia (SIBI), was established in 2013. While respiratory illnesses such as SARI pose a significant problem, there are limited incidence‐based data on influenza disease burden in Indonesia. This study aimed to estimate the incidence of influenza‐associated SARI in Indonesia during 2013‐2016 at three existing SIBI surveillance sites.MethodsFrom May 2013 to April 2016, inpatients from sentinel hospitals in three districts of Indonesia (Gunung Kidul, Balikpapan, Deli Serdang) were screened for SARI. Respiratory specimens were collected from eligible inpatients and screened for influenza viruses. Annual incidence rates were calculated using these SIBI‐enrolled influenza‐positive SARI cases as a numerator, with a denominator catchment population defined through hospital admission survey (HAS) to identify respiratory‐coded admissions by age to hospitals in the sentinel site districts.ResultsFrom May 2013 to April 2016, there were 1527 SARI cases enrolled, of whom 1392 (91%) had specimens tested and 199 (14%) were influenza‐positive. The overall estimated annual incidence of influenza‐associated SARI ranged from 13 to 19 per 100 000 population. Incidence was highest in children aged 0‐4 years (82‐114 per 100 000 population), followed by children 5‐14 years (22‐36 per 100 000 population).ConclusionsIncidence rates of influenza‐associated SARI in these districts indicate a substantial burden of influenza hospitalizations in young children in Indonesia. Further studies are needed to examine the influenza burden in other potential risk groups such as pregnant women and the elderly.
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
<span>Machine learning has been introduced in the sphere of the medical field to enhance the accuracy, precision, and analysis of diagnostics while reducing laborious jobs. With the mounting evidence, machine learning has the capability to detect mental distress like depression. Since depression is the most prevalent mental disorder in our society at present, and almost the majority of the population suffers from this issue. Hence there is an extreme need for the depression detection models, which will provide a support system and early detection of depression. This review is based on the image and video-based depression detection model using machine learning techniques. This paper analyses the data acquisition techniques along with their databases. The indicators of depression are also reviewed in this paper. The evaluation of different researches, along with their performance parameters, is summarized. The paper concludes with remarks about the techniques used and the future scope of using the image and video-based depression prediction. </span>
Augmented Reality (AR) is a technology that combines 2 dimensions with 3 dimensions in realtime. Augmented reality can be applied in various things, one of them is in the field of education. In the world of augmented reality technology education can be used as a means of introduction of aquatic animals. Augmented reality serves to display 3-dimensional objects and their information by scanning markers. Markers made as scanning objects are carried out by the smartphone camera and will display objects in 3 dimensions. Based on testing the distance measurement of the camera against the marker, the optimal distance obtained from the camera can read the marker at a distance of 8 - 77 cm. This augmented reality application has been running well on smartphones and has featured 3-dimensional objects along with its information. Applications that have been made to facilitate the introduction of aquatic animals.
<p>ABSTRAK <br /> <br />Perkembangan teknologi di segala aspek kehidupan saat sekarang ini sangat dibutuhkan, hal ini dapat dilihat dari banyaknya teknologi-teknologi sudah menggantikan pekerjaan-pekerjaan manusia yang dilakukan secara manual yang memakan tenaga dan waktu. Seperti pada aquarium ikan yang ada, pekerjaan yang rutin dilakukan pada Aquarium adalah mengganti air yang ada didalamnya agar<br />terlihat bersih dan menciptakan kondisi yang baik untuk ikan tersebut. Biasanya akan dibuat suatu jadwal untuk mengganti air aquarium tersebut, hal ini terkadang sangat menyita waktu apalagi pada saat kesibukan meningkat dan jika telat ataupun lupa untuk mengganti air pada aquarium tersebut maka dapat berakibat buruk pada kondisi air dan juga ikan yang ada didalamnya. maka penulis tertarik untuk perancang Alat Pengganti Air Aquarium Otomatis Berbasis Mikrokontroler ATmega8, merupakan sebuah aplikasi alat yang digunakan untuk otomatisasi penggantian air pada aquarium. Alat yang dirancang ini menggunakan RTC dan sensor kekeruhan sebagai penentu kapan air aquarium akan berganti, serta digunakan 2 buah pompa air mini untuk menguras dan mengisi air aquarium. Alat ini dirancang untuk mempermudah pengguna aquarium dalam hal penggantian air.</p>
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