【Special Selection】”AI”+ Agriculture/ Medicare/ Social Networks...

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Artificial Intelligence has been widely applied to various fields, including Precision Agriculture, Intelligent Medicare, Industrial Production, Social Networks, etc. This special selection includes newly application studies of AI published in IJAC within the recent two years. Download full text now!

Part One: AI+ Agriculture

1.Open Access

Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture 

Tian-Xiang Zhang, Jin-Ya Su, Cun-Jia Liu, Wen-Hua Chen

Brief introduction:

The main contributions of the work are:

1) The remote sensing images of the newly launched Sentinel-2A satellite are exploited for the purpose of land cover classification by using different features with supervised learning algorithm.

2) It is discovered that the approach based on selected bands using MI algorithm can increase the classification accuracy than index-based and index-related approach. It can also obtain the comparative performance as the one based on all bands available on Sentinel-2A satellite.

3) By considering the balance between time consuming and classification accuracy, full bands approach can be employed to achieve the higher accuracy in a small

Download at:

https://link.springer.com/article/10.1007/s11633-018-1143-x

http://www.ijac.net/en/article/doi/10.1007/s11633-018-1143-x

Chinese introduction:

https://mp.weixin.qq.com/s/77gLYoG1e0KWeu1kj3WhGg

 

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2.Localization and Classification of Rice-grain Images Using Region Proposals-based Convolutional Neural Network

Kittinun Aukkapinyo, Suchakree Sawangwong, Parintorn Pooyoi, Worapan Kusakunniran

Brief introduction:

This paper proposes a framework to develop data models that can classify and localize each rice grain in an input image. The data model is trained using mask RCNN with pre-trained weights of COCO dataset. The proposed framework is mostly an iterative process which consists of data acquisition, data preparation, data modeling, and model evaluation. There are 5 types of Thai rice grains which are used in this research. Several models are constructed to localize and classify each grain in an image in various scenarios. The best performance model is for classifying sticky and paddy rice grains since it can achieve mAP of 1.0 when rice grains in an image are manually aligned. 

Download at:

https://link.springer.com/article/10.1007/s11633-019-1207-6

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1207-6

 

 

Part Two: AI+ Medicare

 

1.Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection

Carlos Beltran-Perez, Hua-Liang Wei, Adrian Rubio-Solis

Brief introduction:

This work puts forward a novel image processing framework for feature extraction based on an improved version of RBF networks. We add to this framework the advantages of the DCT to compress information. Finally, we successfully adapt the MSRBF methodology to CAD systems for breast cancer detection.

Download at:

https://link.springer.com/article/10.1007/s11633-019-1210-y

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1210-y

Chinese introduction:

https://mp.weixin.qq.com/s/XpG1Y620VesoTnbcagNAbA

 

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2.Review:

Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey

Zhen-Jie Yao, Jie Bi, Yi-Xin Chen

Brief introduction:

In the recent years, deep learning models have addressed many problems in various fields. Meanwhile, technology development has spawned the big data in healthcare rapidly. Nowadays, application of deep learning to solve the problems in healthcare is a hot research direction. This paper introduces the application of deep learning in healthcare extensively. We focus on 7 application areas of deep learning, which are electronic health records (EHR), electrocardiography (ECG), electroencephalogram (EEG), community healthcare, data from wearable devices, drug analysis and genomics analysis. The scope of this paper does not cover medical image processing since other researchers have already substantially reviewed it. In addition, we analyze the merits and drawbacks of the existing works, analyze the existing challenges, and discuss future trends.

Download at:

https://link.springer.com/article/10.1007/s11633-018-1136-9

http://www.ijac.net/en/article/doi/10.1007/s11633-018-1136-9

Chinese introduction:

https://mp.weixin.qq.com/s/4E2SGaelNfR7FZaXF5vEhQ

 

 

Part Three: AI+ Social Networks

 

1.Text-mining-based Fake News Detection Using Ensemble Methods

Harita Reddy, Namratha Raj, Manali Gala, Annappa Basava

Brief introduction:

Social media is a platform to express one’s views and opinions freely and has made communication easier than it was before. This also opens up an opportunity for people to spread fake news intentionally. The ease of access to a variety of news sources on the web also brings the problem of people being exposed to fake news and possibly believing such news. This makes it important for us to detect and flag such content on social media. With the current rate of news generated on social media, it is difficult to differentiate between genuine news and hoaxes without knowing the source of the news. This paper discusses approaches to detection of fake news using only the features of the text of the news, without using any other related metadata. We observe that a combination of stylometric features and text-based word vector representations through ensemble methods can predict fake news with an accuracy of up to 95.49%.

Download at:

https://link.springer.com/article/10.1007/s11633-019-1216-5

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1216-5

 

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2.Open Access Review:

Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks

Brian D. O. Anderson, Mengbin Ye

Brief introduction:

A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individual′s self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case, where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual′s expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create “pluralistic ignorance”, where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure, disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals.

Download at:

https://link.springer.com/article/10.1007/s11633-019-1169-8

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1169-8

Chinese introduction:

https://mp.weixin.qq.com/s/vaos5lRYY6WwtQTJF9JjDw

 

 

Part Four: AI+ Industrial Production

 

1.Open Access Review:

Electronic Nose and Its Applications: A Survey

Diclehan Karakaya, Oguzhan Ulucan, Mehmet Turkan

Brief introduction:

In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.

Download at:

https://link.springer.com/article/10.1007/s11633-019-1212-9

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1212-9

Chinese introduction:

https://mp.weixin.qq.com/s/2MLt-kALOjUL4bxXEr6RLQ

 

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2.Open Access Review:

Toolpath Interpolation and Smoothing for Computer Numerical Control Machining of Freeform Surfaces: A Review

Wen-Bin Zhong, Xi-Chun Luo, Wen-Long Chan, Yu-Kui Cai, Fei Ding, Hai-Tao Liu, Ya-Zhou Sun

Brief introduction:

Driven by the ever increasing demand in function integration, more and more next generation high value-added products, such as head-up displays, solar concentrators and intra-ocular-lens, etc., are designed to possess freeform (i.e., non-rotational symmetric) surfaces. The toolpath, composed of high density of short linear and circular segments, is generally used in computer numerical control (CNC) systems to machine those products. However, the discontinuity between toolpath segments leads to high-frequency fluctuation of feedrate and acceleration, which will decrease the machining efficiency and product surface finish. Driven by the ever-increasing need for high-speed high-precision machining of those products, many novel toolpath interpolation and smoothing approaches have been proposed in both academia and industry, aiming to alleviate the issues caused by the conventional toolpath representation and interpolation methods. This paper provides a comprehensive review of the state-of-the-art toolpath interpolation and smoothing approaches with systematic classifications. The advantages and disadvantages of these approaches are discussed. Possible future research directions are also offered.

Download at:

https://link.springer.com/article/10.1007/s11633-019-1190-y

http://www.ijac.net/en/article/doi/10.1007/s11633-019-1190-y

Chinese introduction:

https://mp.weixin.qq.com/s/PqO0D5-TNE-aKuTPGVZs3w

 

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3.Review:

An Overview of Contour Detection Approaches

Xin-Yi Gong, Hu Su, De Xu, Zheng-Tao Zhang, Fei Shen, Hua-Bin Yang

Brief introduction:

Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories: pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks (DCNNs) have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.

Download at:

https://link.springer.com/article/10.1007/s11633-018-1117-z

http://www.ijac.net/en/article/doi/10.1007/s11633-018-1117-z

Chinese introduction:

https://mp.weixin.qq.com/s/3zd10q-x56sU0We_5HAvJg

 

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