scholarly journals Mapping knowledge structure of artificial intelligence research in Bangladesh based on co-word analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Rajesh Kumar Das ◽  
Mohammad Sharif Ul Islam
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Knowledge Structure ◽  
Research Field ◽  
Intellectual Structure ◽  
Research Trend ◽  
Support Vector ◽  
Knowledge Domain ◽  
Machine Learning Classification ◽  
Word Analysis

Purpose: This article aims to map the knowledge structure of artificial intelligence (AI) in Bangladesh through detecting the interdisciplinarity and topic hotspots in the light of co-word analysis. Methodology: This study adopted bibliometric analysis of publications collected from the Web of Science (WoS) database. The WoS database was searched and 1557 publications were found. 1359 papers were selected for final analysis after eliminating duplicates. Co-occurrence words matrix, keyword clusters, hot topics were mapped using co-word analysis. The results were mapped, clustered and presented by VOSviewer.Results: The result showed a rapidly increasing publication trajectory with 12 sub-domain cluster under the AI knowledge domain in Bangladesh. It also identified that AI, machine learning, classification, neural network, deep learning, artificial neural network, convolutional neural network, support vector machine and data mining are hot topics during the period of studied time. However, the findings also suggest that many research areas in the research domain of AI of Bangladesh is still nascent.Limitation: VOSviewer often avoid having overlapping terms when multiple terms are positioned very close to each other. So, the overlapping terms remain invisible sometimes.Practical implications: This study may have potential usefulness in uncovering the AI research fields’ intellectual structure within a discipline and also to anticipate future innovation pathways of AI field in Bangladesh.Originality: Bibliometric methods to explore the research trend and growth of AI research field as a ‘knowledge base’ in Bangladesh is one of the first attempts.

scholarly journals Artificial intelligence techniques applied to predict teams position of the Brazilian football championship

1989 ◽  
Author(s):  
Mariana Kleina ◽  
◽  
Mateus Noronha dos Santos ◽  
Tiago Noronha dos Santos ◽  
Marcos Augusto Mendes Marques ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Group Classification ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Post Processing ◽  
Specific Training ◽  
Reasonable Prediction ◽  
True Group ◽  
Better Than

This study presents a classifier prediction in groups for the Brazilian Football Championship of both A and B leagues, from the results of the first half of each championship. With assertive predictions of the group where a team will end the championship, strategic planning can be performed in the squad, such as new hiring, specific training for athletes, and possible championships that the team will be entitled to participate in according to the group classification. In order to find the predictions, two techniques of artificial intelligence were applied: Multi-Layer Perceptron (MLP), which is a type of artificial neural network, and Support Vector Machine (SVM). Preliminary results show that the proposed methodology is very promising, with more than 40% successful cases with MLP and almost 50% with SVM. Moreover, results indicate that the methodology is able to make a reasonable prediction by missing one group of the true group at the end of the championship. The SVM technique was slightly better than MLP. A post-processing analysis of the SVM results was applied to the 2018 A league data from the Brazilian championship, resulting in 85% success indicator of groups.

scholarly journals Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6241
Author(s):  
Israel Campero-Jurado ◽  
Sergio Márquez-Sánchez ◽  
Juan Quintanar-Gómez ◽  
Sara Rodríguez ◽  
Juan M. Corchado
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Health And Safety ◽  
Working Environment ◽  
Training Dataset ◽  
Support Vector ◽  
Industrial Internet Of Things ◽  
Industrial Internet

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

scholarly journals Prediction of the Wall Factor of Arbitrary Particle Settling through Various Fluid Media in a Cylindrical Tube Using Artificial Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mingzhong Li ◽  
Guodong Zhang ◽  
Jianquan Xue ◽  
Yanchao Li ◽  
Shukai Tang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Prediction Models ◽  
Cylindrical Tube ◽  
Rectangular Prism ◽  
Support Vector ◽  
Hydrodynamic Drag ◽  
Artificial Neural

Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results.

scholarly journals Classification of Street Tree Species Using UAV Tilt Photogrammetry

2021 ◽  
Vol 13 (2) ◽  
pp. 216
Author(s):  
Yutang Wang ◽  
Jia Wang ◽  
Shuping Chang ◽  
Lu Sun ◽  
Likun An ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Bp Neural Network ◽  
Tree Species ◽  
Support Vector ◽  
Street Trees ◽  
Species Classification ◽  
Machine Learning Classification ◽  
Tree Species Classification ◽  
Street Tree

As an important component of the urban ecosystem, street trees have made an outstanding contribution to alleviating urban environmental pollution. Accurately extracting tree characteristics and species information can facilitate the monitoring and management of street trees, as well as aiding landscaping and studies of urban ecology. In this study, we selected the suburban areas of Beijing and Zhangjiakou and investigated six representative street tree species using unmanned aerial vehicle (UAV) tilt photogrammetry. We extracted five tree attributes and four combined attribute parameters and used four types of commonly-used machine learning classification algorithms as classifiers for tree species classification. The results show that random forest (RF), support vector machine (SVM), and back propagation (BP) neural network provide better classification results when using combined parameters for tree species classification, compared with those using individual tree attributes alone; however, the K-nearest neighbor (KNN) algorithm produced the opposite results. The best combination for classification is the BP neural network using combined attributes, with a classification precision of 89.1% and F-measure of 0.872, and we conclude that this approach best meets the requirements of street tree surveys. The results also demonstrate that optical UAV tilt photogrammetry combined with a machine learning classification algorithm is a low-cost, high-efficiency, and high-precision method for tree species classification.

scholarly journals An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

Materials ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5342
Author(s):  
Sunil Kumar Panigrahy ◽  
Yi-Chieh Tseng ◽  
Bo-Ruei Lai ◽  
Kuo-Ning Chiang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Electronic Packaging ◽  
Prediction Accuracy ◽  
Chip Thickness ◽  
Design Parameters ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Wafer Level ◽  
Advanced Packaging

Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

scholarly journals Automatically Extracting Disease-Disease Association from Literature with a Large Margin Context-Aware Convolutional Neural Network (Preprint)

2019 ◽  
Author(s):  
Po-Ting Lai ◽  
Wei-Liang Lu ◽  
Ting-Rung Kuo ◽  
Chia-Ru Chung ◽  
Jen-Chieh Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Disease Association ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Context Aware ◽  
Large Margin ◽  
Machine Learning Classification ◽  
Output Layer

BACKGROUND Research on disease-disease association, like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest disease association findings. For one thing, comorbidity and complication keywords pull up large numbers of PubMed studies. Secondly, disease is not highlighted in search results. Third, disease-disease association (DDA) is not identified, as currently no DDA extraction dataset or tools are available. OBJECTIVE Since there are no available disease-disease association extraction (DDAE) datasets or tools, we aim to develop (1) a DDAE dataset and (2) a neural network model for extracting DDAs from literature. METHODS In this study, we formulate DDAE as a supervised machine learning classification problem. To develop the system, we first build a DDAE dataset. We then employ two machine-learning models, support vector machine (SVM) and convolutional neural network (CNN), to extract DDAs. Furthermore, we evaluate the effect of using the output layer as features of the SVM-based model. Finally, we implement large margin context-aware convolutional neural network (LC-CNN) architecture to integrate context features and CNN through the large margin function. RESULTS Our DDAE dataset consists of 521 PubMed abstracts. Experiment results show that the SVM-based approach achieves an F1-measure of 80.32%, which is higher than the CNN-based approach (73.32%). Using the output layer of CNN as a feature for SVM does not further improve the performance of SVM. However, our LC-CNN achieves the highest F1-measure of 84.18%, and demonstrates combining the hinge loss function of SVM with CNN into a single NN architecture outperforms other approaches. CONCLUSIONS To facilitate the development of text-mining research for DDAE, we develop the first publicly available DDAE dataset consisting of disease mentions, MeSH IDs and relation annotations. We develop different conventional ML models and NN architectures, and evaluate their effects on our DDAE dataset. To further improve DDAE performance, we propose an LC-CNN model for DDAE that outperforms other approaches.

scholarly journals An Empirical Study on Sports Combination Training Action Recognition Based on SMO Algorithm Optimization Model and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hecai Jiang ◽  
Sang-Bing Tsai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Action Recognition ◽  
Optimization Model ◽  
Recognition Rate ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Module Design ◽  
3D Data ◽  
Smo Algorithm

In order to improve the accuracy of sports combination training action recognition, a sports combination training action recognition model based on SMO algorithm optimization model and artificial intelligence is proposed. In this paper, by expanding the standard action data, the standard database of score comparison is established, and the system architecture and the key acquisition module design based on 3D data are given. In this paper, the background subtraction method is used to process the sports video image to obtain the sports action contour and realize the sports action segmentation and feature extraction, and the artificial intelligence neural network is used to train the feature vector to establish the sports action recognition classifier. This paper mainly uses a three-stream CNN artificial intelligence deep learning framework based on convolutional neural network and uses a soft Vlad representation algorithm based on data decoding to learn the action features. Through the data enhancement of the existing action database, it uses support vector machine to achieve high-precision action classification. The test results show that the model improves the recognition rate of sports action and reduces the error recognition rate, which can meet the online recognition requirements of sports action.

The Makerspace Phenomenon: A Bibliometric Review of Literature (2012–2020)

2021 ◽  
pp. 2150006
Author(s):  
Gautam Sharma
Keyword(s):  
Citation Analysis ◽  
Research Field ◽  
Intellectual Structure ◽  
Review Of Literature ◽  
Main Research ◽  
Word Analysis ◽  
Research Themes ◽  
Bibliometric Review ◽  
Programming Knowledge ◽  
Over Time

This paper uses bibliometric methods to review the research on makerspaces. The objective of the study was to document the growth and geographic distribution of makerspace literature, identify the main authors, documents. In addition to this, the study also combined two bibliometric analysis methods, co-citation analysis and co-word analysis to identify the intellectual structure of the makerspace knowledge base and the evolution of research themes over time. A total of 654 documents related to makerspaces between 2012 and 2020 were identified from the Scopus database. The review found that research base on makerspace is starting to grow from 2017 onwards with an accelerating growth rate, however, the published studies are mainly from USA and Europe. The paper also lists the most cited documents, the influential sources of publications and the main authors working on this area. The review also identified five research clusters using co-citation analysis that have emerged over time which are “innovation and development in makerspaces”, “child development in makerspaces”, “learning and STEM education in makerspaces”, “implementation of makerspaces in education”, and “university makerspaces”. Another major finding highlighted “innovation, design, and creativity”, “engineering curriculum”, “skill development”, “computer programming knowledge”, and “learning, collaboration and community development” as the five main research themes using the co-word analysis. These findings provide a robust roadmap for further investigation in this research field.

scholarly journals Research on the emotional cognitive evaluation model based on artificial neural network

2020 ◽  
Vol 145 ◽  
pp. 01040
Author(s):  
Qiying Gan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Information Processing ◽  
Evaluation Model ◽  
Research Field ◽  
Intelligent Information Processing ◽  
The Neural Network ◽  
Artificial Neural ◽  
Intelligent Information

the neural network, fuzzy set theory and evolutionary algorithm in artificial intelligence are all intelligent information processing theories that follow the biological processing mode. These theories are realized by rational logical thinking mode without considering the role of human perceptual thinking in the information processing process, such as emotion and cognition. Among them, the neural network mainly imitates the function of the mental system of human, adopts the method from the bottom to the top, and processes the difficult language pattern information through a large number of complicated connections of neurons. Artificial neural network (Ann) is a cross research field of artificial intelligence and life science. This theory mainly imitates the information processing mechanism of organisms in nature and is mainly used in intelligent information processing systems that can adapt to long-term changes in the environment. Therefore, neural network has important application significance in the research of intelligence, robot and artificial emotion.

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