Design and development of learning model for compression and processing of deoxyribonucleic acid genome sequence

Owing to the substantial volume of human genome sequence data files (from 30-200 GB exposed) Genomic data compression has received considerable traction and storage costs are one of the major problems faced by genomics laboratories. This involves a modern technology of data compression that reduces not only the storage but also the reliability of the operation. There were few attempts to solve this problem independently of both hardware and software. A systematic analysis of associations between genes provides techniques for the recognition of operative connections among genes and their respective yields, as well as understandings into essential biological events that are most important for knowing health and disease phenotypes. This research proposes a reliable and efficient deep learning system for learning embedded projections to combine gene interactions and gene expression in prediction comparison of deep embeddings to strong baselines. In this paper we preform data processing operations and predict gene function, along with gene ontology reconstruction and predict the gene interaction. The three major steps of genomic data compression are extraction of data, storage of data, and retrieval of the data. Hence, we propose a deep learning based on computational optimization techniques which will be efficient in all the three stages of data compression.

Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction

As an emerging weakly supervised learning framework, partial label learning considers inaccurate supervision where each training example is associated with multiple candidate labels among which only one is valid. In this article, a first attempt toward employing dimensionality reduction to help improve the generalization performance of partial label learning system is investigated. Specifically, the popular linear discriminant analysis (LDA) techniques are endowed with the ability of dealing with partial label training examples. To tackle the challenge of unknown ground-truth labeling information, a novel learning approach named Delin is proposed which alternates between LDA dimensionality reduction and candidate label disambiguation based on estimated labeling confidences over candidate labels. On one hand, the (kernelized) projection matrix of LDA is optimized by utilizing disambiguation-guided labeling confidences. On the other hand, the labeling confidences are disambiguated by resorting to k NN aggregation in the LDA-induced feature space. Extensive experiments over a broad range of partial label datasets clearly validate the effectiveness of Delin in improving the generalization performance of well-established partial label learning algorithms.

An Empirical Analysis of Learners’ Experience on Select MOOCs Platforms with Reference to Users Reviews and Ratings

Massive open online courses (MOOCs) is created greater prominence as a modern learning system mainly due to the advanced progress made in the area of Learning and Teaching Technology and. Covid pandemic also had open opportunities for Online Learning Platforms. Present study has focused on learners’ experience with various MOOCs platforms through online reviews and ratings, which were collected from Google play store and appbot application. Seven MOOCs platforms namely Coursera, edX, Udemy, Swayam, LinkedIn , Khan Academy and Upgrad are reviewed in this paper. The main objective is to compare the select MOOCs platforms in the area of users’ experience. Total number of reviews and rating has been taken for the study is 63, 652. The time frame of sample data was taken for last one year that is from 5th April, 2020 to 5th April, 2021. Sentiment analysis and chi-square test is applied to analyze the difference among the different MOOCs platforms. The major outcomes were the reviews and ratings of different platform found with very good uses experience.

A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.

ANALISA PENGARUH PELATIHAN MEDIA PEMBELAJARAN ONLINE PADA GURU SEKOLAH DASAR NEGERI 2 KAWAN BANGLI

With the current outbreak of the corona virus, all sectors must start to improve so that they can survive during a pandemic, including the education sector. The teaching and learning process carried out during a pandemic situation like this is carried out using an online system and distance learning. Teachers must be able to provide facilities so that students can absorb the material optimally. Another problem that arises is that not all teachers are able to implement the distance learning system and also using the applications or technologies that support distance learning system. To solve this problem, the school needs to get the training of how to make a distance learning system media and in this case the school cooperates with STMIK STIKOM INDONESIA (STIKI) for the training. The training materials provided are google classroom training, google form and training on making the learning videos. The results of the analysis were obtained from the pretest and posttest questionnaires which were described descriptively. After receiving the training, most of the teachers will switch to use the Google Classroom media instead of WA social media, because 100% of the teachers stated that Google Classroom is very helpful in supporting online learning. In addition, according to the posttest questionnaires, 8.3% of the teachers understand very well about the features contained in Google Classroom, 41.7% understand it well and 50% are understand it quite well, meaning that most of the teachers understand the use of Google Classroom. The training also received a good responses from SDN 2 Kawan School and it will be applied to support their distance learning system.

EVALUATION OF DISTANCE LEARNING SYSTEM (E-LEARNING): A SYSTEMATIC LITERATURE REVIEW

Research in the field of e-learning is currently experiencing rapid development, especially due to the covid 19 pandemic. The application of e-learning in the world of education is currently the main thing and requires evaluation of its use. This study aims to determine the evaluation of models and trends in the development of e-learning (Learning Management System). Applying the Kitchenham approach, this System Literature Review (SLR) uses three main databases including Science Direct, ACM, SCOPUS. The final result obtained 38 articles published between 2016 and 2021. From this SLR, it was found that there were 7 criteria, namely Platform, Evaluation Model, Evaluation, Model, Approach, Problem, Trend and Challenge. These 7 criteria can be used for further research on e-learning. Thus this research provides knowledge about criteria that can be used further in research on E-learning and provides insight into its state-of-the-art.

PEMBINAAN PEMBELAJARAN DARING SELAMA PANDEMI COVID 19 Di SMK PERMATA HARAPAN BATAM

This service aims to provide online learning guidance during the COVID-19 pandemic which was carried out at the Permata Harapan Vocational School in Batam. This training was carried out for 4 meetings using the zoom application. Community service activities in the form of educational development for Permata Harapan Vocational High School students can provide benefits by applying the methods of implementing training activities, discussions and questions and answers. This service was attended by Mr. Miftahul Ilmi and Mrs. Lolita as teachers at Permata Harapan school and also attended by 20 students. The results obtained from this activity are: the ability of students to the digital world increases, especially for the applications used and the available tools. Students also feel they have a high curiosity to understand the applications used and are interested in the menus offered. The ability to adapt to the online learning system is very necessary for current conditions, where the presenter also conveys motivation to students to continue to be able to follow the lesson well.