2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC)最新文献_第3页

Smartening E-therapy using Facial Expressions and Deep Learning 使用面部表情和深度学习的智能电子疗法

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334115

God's Gift G. Uzor, Hima Vadapalli

{"title":"Smartening E-therapy using Facial Expressions and Deep Learning","authors":"God's Gift G. Uzor, Hima Vadapalli","doi":"10.1109/IMITEC50163.2020.9334115","DOIUrl":"https://doi.org/10.1109/IMITEC50163.2020.9334115","url":null,"abstract":"Emotional intelligence finds its application in several fields, and researchers are currently looking to explore the possibility for computers to demonstrate such intelligence. Examining human facial expressions, subject to the activities they carry out at certain times can help improve interactions between humans and computers especially in the era of a digitized society. Communication channels include vocal, body gestures, and facial expressions. Body gestures and facial expressions, as a means of communication, are known to be acquired either involuntarily or voluntarily to lay emphasis on emotions that may not be explicitly expressed via vocal means. Facial expressions are one of the common non-verbal visual cues used by humans in communicating emotions. Facial expressions as a channel to estimate emotions is useful in many applications such as e-learning, online marketing, and e-therapy. E-therapy is regarded as having a healthcare professional to provide mental health services via an electronic medium. There happens to be a range of challenges that could prompt therapy to be administered via electronic channels. This study explores the development of a tool that can facilitate the evaluation of a patient's emotion using their facial expressions during an e-therapy session. Further to evaluating facial expressions, there is a medium provided to estimate the expressions and generate a feedback that can be used by the therapist. Models for facial expression estimation and feedback generation uses deep learning and transfer learning techniques. The initial study was carried out using expression samples obtained from the KDEF and JAFFE databases. The results obtained show a 74.9% and 90.9% accuracy in facial expression classification of images from KDEF and JAFFE databases respectively.","PeriodicalId":349926,"journal":{"name":"2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131724560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Phylogenomics for Tracking the Epidemiology of COVID-19: The Genomic Data Gap for the African Continent 用于跟踪COVID-19流行病学的系统基因组学:非洲大陆的基因组数据缺口

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334111

A. Adebowale, Precious K. Letebele

{"title":"Phylogenomics for Tracking the Epidemiology of COVID-19: The Genomic Data Gap for the African Continent","authors":"A. Adebowale, Precious K. Letebele","doi":"10.1109/IMITEC50163.2020.9334111","DOIUrl":"https://doi.org/10.1109/IMITEC50163.2020.9334111","url":null,"abstract":"The COVID-19 pandemic has disrupted health systems the world over, resulting in the loss of many lives and destabilising economies. Phylogenomic tracking of the pandemic represents one of the ways to monitor its spread in real-time. However, effective phylogenomic monitoring is dependent on the generation and analysis of rich genomic datasets. In this study, we performed phylogenetic analysis on SARS-CoV-2 genome data for the African continent to illustrate the spread of the pandemic. Africa's contribution to the SARS-CoV-2 genome data stands at just under 2% of the global total, with only seven countries currently represented on the NCBI virus database, and 16 countries on the GISAID database, as of 10 August 2020. A large portion of the data (79%) in NCBI is from Egypt, while sequence data from South Africa (48%) dominates the GISAID collection. Although there exist a massive data gap in terms of geographic coverage and scale across both databases, the inferred phylogeny is consistent with Egypt having the first reported case of COVID-19 on the continent, with multiple independent infections in other parts of Africa. However, we identify significant incongruences in the timing of sampling and placement of sequence on the inferred phylogeny. We surmise that the source of incongruence is a probable discrepancy between sample collection and sequence generation, leading to phylogenetic placements that violate basic rule of molecular evolutionary progression. Consequently, we propose the rapid processing of samples destined for sequencing as soon as they are collected, as Africa gradually increases its SARS-CoV-2 genomic footprint. We also advocate for the release of SARS-CoV-2 genomic sequences to the public domain to facilitate quality research around the virus.","PeriodicalId":349926,"journal":{"name":"2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134071434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gamification of Functional Programming 函数式编程的游戏化

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334096

Tavonga Delroy Chifamba, Yusuf Moosa Motara

引用次数: 1

TalkSQL: A Tool for the Synthesis of SQL Queries from Verbal Specifications TalkSQL:一个从口头说明合成SQL查询的工具

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334088

George Obaido, Abejide Ade-Ibijola, Hima Vadapalli

{"title":"TalkSQL: A Tool for the Synthesis of SQL Queries from Verbal Specifications","authors":"George Obaido, Abejide Ade-Ibijola, Hima Vadapalli","doi":"10.1109/IMITEC50163.2020.9334088","DOIUrl":"https://doi.org/10.1109/IMITEC50163.2020.9334088","url":null,"abstract":"Recent advances in the field of Natural Language Processing (NLP) have led to many robust user interfaces (UIs) designed as intelligent tutoring systems (ITS) that help students learn, query and access data in relational databases. Such tools are generally referred to as Natural Language Interfaces to Databases (NLIDBs). Many of these UIs rely on voice or typewritten for further processing. Research has shown that typewritten remains the preferred input method used by database UIs designers for querying relational databases due to its flexibility. Still, there is a dearth of tools that require voice-based inputs for querying relational databases. Despite the scarcity of these tools, many of them fail to provide a comprehensive feedback to a user. In this paper, we introduce a voice-based query system named TalkSQL that takes voice inputs from a user, converts these words into SQL queries and returns a feedback to the user. Automatic feedback generation is of immense importance. To achieve this, we have used regular expressions, a representation of regular languages for the recognition of the Create, Read, Update, Delete (CRUD) operations in SQL and automatically generate a feedback using pre-defined templates. A survey on 53 participants showed that 91.2% agreed that they were able to understand the CRUD command using TalkSQL. The expected contributions are in two-fold: this work may assist a special (e.g. visually impaired) learner to understand SQL queries, and show that a voice-based interface can assist users in understanding SQL queries.","PeriodicalId":349926,"journal":{"name":"2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC)","volume":" 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113948110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Semantic Web-Based Blueprint for Digital Healthcare in A Resource Constrained Environment: Towards A Connected Healthcare Ecosystem 资源受限环境下基于语义的数字医疗蓝图:迈向互联医疗生态系统

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334079

Ludovic Tonguo, P. Mvelase, A. Coleman

引用次数: 1

Data Mining and Artificial Intelligence Techniques Used to Extract Big Data Patterns 用于提取大数据模式的数据挖掘和人工智能技术

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334069

Taetse Durand, M. Hattingh

{"title":"Data Mining and Artificial Intelligence Techniques Used to Extract Big Data Patterns","authors":"Taetse Durand, M. Hattingh","doi":"10.1109/IMITEC50163.2020.9334069","DOIUrl":"https://doi.org/10.1109/IMITEC50163.2020.9334069","url":null,"abstract":"A lot of research and analysis has been done that focuses on the implementation, use, and evaluation of artificial intelligence techniques. The analysis is done on different techniques and variations of known methods regarding their characteristics like speed, performance, and effectiveness using scientific methods, statistics and mathematical proofs. On the other end of the spectrum, a lot of research has been done on high-level data mining as well. The research on data mining usually stops at technical implementations and focuses mainly on high-level techniques to manipulate the bulk of data to be mined. The physical implementation is usually abstracted and left for libraries to optimize. In order to use this research in the area of big data, the areas of AI and Data mining need to be conjoined so that the appropriate knowledge from both technical and conceptual areas is used. The purpose of this literature review is to systematically review the research done on both the technical and conceptual ends of the spectrum and to find the overlapping techniques. This is needed to get a clear understanding of the entire knowledge extraction process from big data to business value. The research results in a broad view of all techniques and their appropriateness towards big data. In order to make decisions on the techniques used for a specific data mining problem, a broad view of all available solutions is needed. This paper attempts to deliver it by investigating all possibilities and discuss their advantages and disadvantages relating to big data.","PeriodicalId":349926,"journal":{"name":"2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114786608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Using Machine Learning Techniques and Matric Grades to Predict the Success of First Year University Students 使用机器学习技术和矩阵分数来预测大学一年级学生的成功

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334087

Nastassja Philippou, Ritesh Ajoodha, Ashwini Jadhav

引用次数: 3

String Diagrams for Modelling Functional Programming 用于建模函数式编程的字符串图

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334072

Y. Motara

引用次数: 0

Estimating Student Learning Affect Using Facial Emotions * 用面部表情估计学生的学习影响*

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334075

B. Zakka, Hima Vadapalli

{"title":"Estimating Student Learning Affect Using Facial Emotions *","authors":"B. Zakka, Hima Vadapalli","doi":"10.1109/IMITEC50163.2020.9334075","DOIUrl":"https://doi.org/10.1109/IMITEC50163.2020.9334075","url":null,"abstract":"The current COVID-19 pandemic has seen a lot of higher institutions of learning embracing the e-learning systems. Although these e-learning systems promise to deliver solutions to teaching and learning in this pandemic era, a key challenge is motivating the learner to engage with the e-learning system continuously. Most e-learners quickly get bored and lose motivation in the course of learning. While there exist many strategies such as chatrooms and sporadic question and answer sessions to keep learners involved in e-learning platforms, they have always achieved minimal connectedness among e-learners. Facial emotions have been identified as an effective tool for interpreting learning experience in learners. This study, therefore, examines the use of facial emotions expressed by learners to interpret their learning affect in an e-learning session. This work also explores a standardized mapping mechanism between facial emotions exhibited and their respective learning affects. The study identifies the physical changes in the face of a learner and uses it to estimate their facial emotions and then based on the mapping mechanism, maps emotional states to a student's learning affect. Experiments include the use of a convolutional neural network for the classification of seven facial emotions. The research study tests different network architectures to find optimal architecture, using the FER2013 dataset. Results from the mapping are statistically analyzed and compared with responses provided by participants who participated in the live testing of the system. Results show that facial emotions, which are a form of non-verbal communication, can be used to estimate the learning affect of a student and provides a new avenue to enhance the current e-learning platforms.","PeriodicalId":349926,"journal":{"name":"2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC)","volume":"42 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Assessing the Credibility of South Africa's Anti-Retroviral Treatment (ART) Eligibility Guidelines using Regression Discontinuity Designs 使用回归不连续设计评估南非抗逆转录病毒治疗(ART)资格指南的可信度

2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC) Pub Date : 2020-11-25 DOI: 10.1109/IMITEC50163.2020.9334077

Kudzai Sibanda, Tapiwa Gundu, Albert Whata

引用次数: 0