The Mekong Delta has made great progress in rice production over the past ten years. Intensive cultivation with multi-cropping brings many benefits to farmers as well as the food export industry. However, this is also an opportunity for raising epidemic outbreak, Brown Plant-hoppers can directly damage by sucking the rice’s vitality, and they can cause the wilting and complete drying of rice plants, a noncontagious disease known as “Hopper-burn”. In this article, we propose the CDNN model for insect classification based on Neural Network and Deep Learning approach. First, insect images are collected and extracted features based on Dense Scale-Invariant Feature Transform. Then, Bag of Features is used for image representation as feature vectors. Lastly, these feature vectors are trained and classified using CDNN model based on Deep Neural Network. The approach is demonstrated with experiments, and measured by a large amount of Brown Plant-hoppers and Ladybugs samples.

The efficiency of a graph visualizing multivariate data is not only subjectively evaluated by human visual perception but also objectively estimated by visual features of graph. For a designed graph, it is necessary to improve visual features to enable human to extract better information from data. Integrating retinal variables into graph is an approach to increasing visual features of graph. In this study, the constituents of graph are grouped into classes of marks by qualitative and quantitative characteristics. The retinal variables are studied and structured to integrate into the classes of marks. A process of five steps is proposed to increase visual features by integrating retinal variables into graph. The process is illustrated with two case studies, increasing visual features of bus space-time map with qualitative mark classes and increasing visual features of the graph representing the data of hand-foot-mouth epidemic in Binhduong with qualitative and quantitative mark classes.

During the recent years, many different methods of using fuzzy time series for forecasting have been published. However, computation in the linguistic environment one term has two parallel semantics, one represented by fuzzy sets (computation-semantics) it human-imposed and the rest (context-semantic) is due to the context of the problem. If the latter semantics is not paid attention, despite the computation accomplished high level of exactly but it has been distorted about semantics. That means the result does not suitable the context of the problem. Hedge Algebras, an algebraic Approach to domains of linguistic variables, unifying the above two semantics of each term, is the basis of convenient calculation in the language environment and does not distort the semantics of terms. A new approach is proposed through a semantic-based algorithm, focus on two key steps: partitioning the universe of discourse of time series into a collection of intervals and mining fuzzy relationships from fuzzy time series, which outperforms accuracy and friendliness in computing.

Hazard map is one of the major parts in smart city model. It gives citizens an overview of a particular hazard in different areas within the city. In this paper, we research and implement an IoT-based air-pollution hazard map system for Ho Chi Minh City. The system consists of sensor and gateway nodes, a server, and maps. The collected sensor values include temperature, dust, carbon monoxide (CO), and carbon dioxide (CO). Data collected by sensors is transmitted to gateway nodes and forwarded to the server. The server saves received data to database and queries according to request from users. A web application have been built to display the data and give users an overview of air pollution state around them.

In this paper, we study fuzzy graph represents by using model theory. We use hedge algebra and linguistic variables for modeling and aggregating two graphs. We prove theorem of limiting in models state space. We also figure out preserved property of aggregation operator.

It is considered an extended notion of the commutativity of the encryption. Using the computational difficulty of the hidden discrete logarithm problem, a new method and post-quantum probabilistic algorithm for commutative encryption are proposed. The finite non-commutative associative algebra containing a large set of the global left-sided unites is used as the algebraic carrier of the proposed method and probabilistic commutative cipher. The latter is secure to the known-plaintext attack and, therefore, efficient to implement on its base a post-quantum no-key encryption protocol. Main properties of the algebraic carrier, which are used in the commutative encryption method, are described.

Geostatistics is briefly concerned with estimation and prediction for spatially continuous phenomena, using data measured at a finite number of spatial locations to estimate values of interest at unmeasured locations. In practice, the costs of installing new observational stations to observe metropolitan water pollution sources, as DO (Dissolvent Oxygen), COD (Chemical Oxygen Demand) and BOD (Biochemical oxygen Demand) concentrations are economically high. In this study, spatial analysis of water pollution of 32 stations monitored during 3 years was carried out. Geostatistics which has been introduced as a management and decision tool by many researchers has been applied to reveal the spatial structure of water pollution fluctuation. In this article, author use the recorded DO concentrations (is the amount of dissolvent oxygen in water required for the respiration of aquatic organisms) at several observational stations on the rivers in Ho Chi Minh City (HCMC), employ the Kriging interpolation method to find suitable models, then predict DO concentrations at some unmeasured stations in the city. Our key contribution is finding good statistical models by several criteria, then fitting those models with high precision. From the data set, author found the best forecast model with the smallest forecast error to predict DO concentration on rivers in Ho Chi Minh City. From there we propose to the authorities to improve areas where DO concentrations exceed permissible levels.

Business cards have been widely used to greet business professionals and exchange contact information. However, the current paper-based way to manage business cards impedes their effective usage, leading to a need for digitalising and extracting business card information. This paper aims to design a business card reader (BCR) application for Android devices. Based on Google vision library, the application digitalises and extracts business card information. We evaluate the application on a dataset of 170 business cards. The results show that the application can digitalise business cards and extract contact information with 88.4% of accuracy. We then further conduct a comparative analysis of our application and other commercial BCR applications. Based on the results, the paper suggests several recommendations for future research.

Water is essential for human life and socio-economic development. Water pollution is a concern for all mankind. In Vietnam, due to the development of factories and factories, water pollution has become more severe, including the Dong Nai River. In this article, the author uses the concentration of TSS at the Tan Hiep control station in Dong Nai River, using the Kriging interpolation method to find the appropriate model and give the results of water pollution prediction. Dong Nai river area over time with high reliability. TSS data were monitored continuously for three months (from the beginning of February 2018 to the end of April 2018), the predicted results using Kriging interpolation with high accuracy with regression coefficient equal to 1,005, the coefficient is 0.859 (the best value is 1), the forecast error is 2.258, the standard error is 0.044. It shows that using the Kriging interpolation method is an effective and suitable solution in mathematical problems with time information.

This study aims to find the description of self-awareness, description of personality typology, and the difference of self-awareness based on personality types in LGBT people. This is a quantitative comparative research. The population of this research is LGBT people in West Sumatera Province Indonesia. Snowball sampling was used in this study. Participants of this research were 224 people from 9 cities in West Sumatera Provinces. Personality typology used two instruments: Millon Personality Type Inventory developed by Airin Triwahyuni for personality typology and Self-Awareness scale developed by the researcher for measuring self-awareness. The data were analyzed using one-way ANOVA technique. The result showed there is significant difference of self-awareness of LGBT people based on personality type, at the level of p=0.03 (p<0.05).