A Systematic Mapping Study of Computer Vision Approaches based on Deep Learning and Neural Network

2017 ◽  
Author(s):  
Eralda Nishani ◽  
Betim Çiço
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping

scholarly journals Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019 ◽  
Vol 9 (15) ◽  
pp. 3196 ◽  
Author(s):  
Lidia María Belmonte ◽  
Rafael Morales ◽  
Antonio Fernández-Caballero
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Systematic Mapping Study ◽  
General View ◽  
Mapping Study ◽  
Vision Systems ◽  
Systematic Mapping ◽  
Aerial Vehicles ◽  
Assistant Robots

Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

scholarly journals Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

2021 ◽  
Vol 11 (9) ◽  
pp. 3986
Author(s):  
Zenun Kastrati ◽  
Fisnik Dalipi ◽  
Ali Shariq Imran ◽  
Krenare Pireva Nuci ◽  
Mudasir Ahmad Wani
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
The Body ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Learning Platform ◽  
Literature Reviews ◽  
Complicated Task

In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

scholarly journals Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 20
Author(s):  
Boštjan Šumak ◽  
Saša Brdnik ◽  
Maja Pušnik
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Sensor Technology ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Human Communication ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Intelligent Interaction

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

scholarly journals Test Smell Detection Tools: A Systematic Mapping Study

2021 ◽  
Author(s):  
Wajdi Aljedaani ◽  
Anthony Peruma ◽  
Ahmed Aljohani ◽  
Mazen Alotaibi ◽  
Mohamed Wiem Mkaouer ◽  
...  
Keyword(s):  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping

scholarly journals Global and Latin American female participation in evidence-based software engineering: a systematic mapping study

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Katia Romero Felizardo ◽  
Amanda Möhring Ramos ◽  
Claudia de O. Melo ◽  
Érica Ferreira de Souza ◽  
Nandamudi L. Vijaykumar ◽  
...  
Keyword(s):  
Software Engineering ◽  
Latin American ◽  
Gender Issue ◽  
Research Area ◽  
Systematic Mapping Study ◽  
Evidence Based ◽  
Mapping Study ◽  
Systematic Mapping ◽  
Participation Of Women ◽  
New Generation

Abstract Context While the digital economy requires a new generation of technology for scientists and practitioners, the software engineering (SE) field faces a gender crisis. SE research is a global enterprise that requires the participation of both genders for the advancement of science and evidence-based practice. However, women across the world tend to be significantly underrepresented in such research, receiving less funding and less participation, frequently, than men as authors in research publications. Data about this phenomenon is still sparse and incomplete; particularly in evidence-based software engineering (EBSE), there are no studies that analyze the participation of women in this research area. Objective The objective of this work is to present the results of a systematic mapping study (SM) conducted to collect and evaluate evidence on female researchers who have contributed to the area of EBSE. Method Our SM was performed by manually searching studies in the major conferences and journals of EBSE. We identified 981 studies and 183 were authored/co-authored by women and, therefore, included. Results Contributions from women in secondary studies have globally increased over the years, but it is still concentrated in European countries. Additionally, collaboration among research groups is still fragile, based on a few women as a bridge. Latin American researchers contribute a great deal to the field, despite they do not collaborate as much within their region. Conclusions The findings from this study are expected to be aggregated to the existing knowledge with respect to women’s contribution to the EBSE area. We expect that our results bring up a reflection on the gender issue and motivate actions and policies to attract female researchers to this area.

IoT-based botnet attacks systematic mapping study of literature

2021 ◽  
Author(s):  
Habiba Hamid ◽  
Rafidah Md Noor ◽  
Syaril Nizam Omar ◽  
Ismail Ahmedy ◽  
Shaik Shabana Anjum ◽  
...  
Keyword(s):  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping
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