Automatic monitoring of pollutants based on biosensors coupled with artificial intelligence

Applications for deep learning in ecology

10.1101/334854 ◽

2018 ◽

Cited By ~ 4

Author(s):

Sylvain Christin ◽

Éric Hervet ◽

Nicolas Lecomte

Keyword(s):

Artificial Intelligence ◽

Pattern Recognition ◽

Deep Learning ◽

Literature Review ◽

Automatic Monitoring ◽

Decision Makers ◽

Applied Ecology ◽

Vast Amount ◽

Research Fields

AbstractA lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.

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Linguistic markers of emotional state of the speech subject (on the problem of automatic monitoring of network communication texts)

Media Linguistics ◽

10.21638/spbu22.2020.405 ◽

2020 ◽

Vol 7 (4) ◽

pp. 428-444

Author(s):

Valerii A. Mishlanov ◽

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Liudmila A. Kadzhaya ◽

Yulia M. Kuznetsova ◽

◽

...

Keyword(s):

Artificial Intelligence ◽

Emotional State ◽

Automatic Monitoring ◽

Electronic Media ◽

Professional Group ◽

Psychological State ◽

Situational Analysis ◽

Implicit Methods ◽

Linguistic Support ◽

Linguistic Markers

This paper discusses linguistic and psychological aspects of the problem of automatic modusdictum analysis of texts published in social networks and other electronic media. Thereupon, theoretical questions are raised anew on the linguistic nature of modus, on the means to express “ego-meanings” in speech, on the differentiation of proper modus (autoreferential signs) and modal-evaluating predicates in dictum position, on the implicit methods of communicating modus information, and the resources to read this information based on discursive speech practices (conventional meanings). The applied goal of the paper is to provide “humanitarian” (psychological and linguistic) support for development of machine “mining” programs, i. e. automatic monitoring of network content and text identification with a certain subjective modality. To achieve this goal, we describe, in particular, such lexical-grammatical features of the texts that can be significant for determining psychological state of an individual or a professional group to identify certain public opinions. Conceptually, this research is connected with the idea of speech system which is manifested both at the level of styles and genres and within independent communicative units, as well as with one of the most important trends in the field of artificial intelligence — the method of relational-situational analysis of texts in natural language. Thematic groups of words (TGW) were compiled including “evaluation collocations” typical of those texts. The templates created on the basis of psychological and linguistic description model suggested in this paper can be used hereafter to develop algorithms for automatic monitoring of the network texts of a given theme (professional stability or mobility, professional crisis, etc.) and evaluation.

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Toward Audio Beehive Monitoring Based on IoT-AI techniques: A Survey and Perspective

Vietnam Journal of Agricultural Sciences ◽

10.31817/vjas.2020.3.1.06 ◽

2020 ◽

Vol 3 (1) ◽

pp. 530-540

Author(s):

Nguyen Huu Du ◽

Nguyen Doan Dong ◽

Vu Thi Luu ◽

Nguyen Van Hoang ◽

Pham Hong Thai ◽

...

Keyword(s):

Artificial Intelligence ◽

Monitoring System ◽

Automatic Monitoring ◽

Recent Time ◽

Negative Effects ◽

New Methods ◽

Monitoring Process ◽

Audio Data ◽

Manual Methods ◽

Bee Colonies

In beekeeping, monitoring beehives plays an important role to make sure the health of bee colonies and to reduce negative effects that could happen for the colonies. A large number of studies have been carried out to improve the performance of monitoring beehives from the traditional manual methods. Especially, the application of Artificial Intelligence (AI) technologies in recent time has led to significant effects in the monitoring process. These new methods, however, have not yet been interested and applied in Vietnam. To understand the use of the AI-based technologies in automatic monitoring beehives, this paper provides a survey on the beehive monitoring system based on audio data and AI techniques. Opportunities and perspectives for the applications of these techniques in audio-based monitoring beehive in Vietnam are also discussed.

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