Edukasi merupakan proses bisnis yang selama berabad telah menjadi pilar pemerintahan dalam membangun bangsa. Proses pelaksanaan edukasi secara global masih belum ada perubahan yang mengikuti perkembangan zaman. Berbagai problem yang dihadapi saat ini oleh pelaksana penyedia jasa edukasi, dalam hal ini sebut saja permasalahan autentikasi dan verifikasi sertifikat yang merupakan luaran mikro maupun makro dari sebuah proses pembelajaran. Adanya mercusuar tentang Blockchain, disebut orang orang sebagai solusi dari masalah polemik pendidikan selama ini. Perpaduan ilmu antara bidang edukasi dan teknologi blockchain akan menciptakan revolusi baru dari industri pendidikan yang dirasa mandek dan tidak sesuai dengan perkembangan zaman saat ini. Terdapat berbagai pencerahan dari solusi ini, namun dirasa belum cukup untuk secara detail menjelaskan ontologi dari edukasi menggunakan blockchain, yang secara keseluruhan membahas tentang keberadaan blockchain for education berdasarkan kajian ilmu. Ontologi for education ini akan berguna sebagai platform literatur bagi peneliti yang akan memulai penelitian dasar dan terapan yang menjurus ke arah blockchain for education. Disamping itu, adanya kelemahan dan inefisiensi dari blockchain yaitu dari segi biaya, ruang penyimpanan, dan kecepatan akan diusulkan solusinya melalui in-chain dan off-chain protocol dalam bentuk framework. Alhasil, penelitian ini juga bisa menjadi landasan platform baru sebagai solusi bagi pemerintah dalam meluncurkan edukasi berbasis blockchain, yang menjadi cikal bakal untuk menciptakan pemerintahan berbasis blockchain.
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>
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