Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

Moral outrage shapes fundamental aspects of social life and is now widespread in online social networks. Here, we show how social learning processes amplify online moral outrage expressions over time. In two preregistered observational studies on Twitter (7331 users and 12.7 million total tweets) and two preregistered behavioral experiments (N = 240), we find that positive social feedback for outrage expressions increases the likelihood of future outrage expressions, consistent with principles of reinforcement learning. In addition, users conform their outrage expressions to the expressive norms of their social networks, suggesting norm learning also guides online outrage expressions. Norm learning overshadows reinforcement learning when normative information is readily observable: in ideologically extreme networks, where outrage expression is more common, users are less sensitive to social feedback when deciding whether to express outrage. Our findings highlight how platform design interacts with human learning mechanisms to affect moral discourse in digital public spaces.

How social learning amplifies moral outrage expression in online social networks.

Wireless sensor network (WSN) comprises of huge amount of sensing nodes being used for collecting the data in different situations. WSN finds applications mostly to gather information from remote places such as in environment monitoring, military, in transportation security and so on. The main problem in WSN is the availability of limited energy resources. For enhancing energy efficiency with the lifespan of sensor nodes, in this research, an energy effective routing protocol, low energy adaptive clustering hierarchy (LEACH) along with optimization algorithm genetic algorithm (GA) has been presented. LEACH is a hierarchical protocol which converts the sensor nodes into cluster heads (CH), and CH gathers and compress the data and send it to the target node. Genetic algorithm helps to find the optimal route by using its fitness function. After simulating the code in MATLAB, the energy consumption rate up to 17.39% has been reduced when GA is used. At last, comparison between proposed work and existing work is performed to determine the efficiency of the proposed work.

Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks

Fish Disease Detection Using Image Based Machine Learning Technique in Aquaculture

With the continuous expansion of aquaculture scale and density, contemporary aquaculture methods have been forced to overproduce resulting in the accelerated imbalance rate of water environment, the frequent occurrence of fish diseases, and the decline of aquatic product quality. Moreover, due to the fact that the average age profile of agricultural workers in many parts of the world are on the higher side, fishery production will face the dilemma of shortage of labor, and aquaculture methods are in urgent need of change. Modern information technology has gradually penetrated into various fields of agriculture, and the concept of intelligent fish farm has also begun to take shape. The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimizing feeding, reducing disease incidences, and accurately harvesting through the idea of "replacing human with machine," so as to liberate the manpower completely and realize the green and sustainable aquaculture. This paper reviews the application of fishery intelligent equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern aquaculture, and analyzes the existing problems and future development prospects. Meanwhile, based on different business requirements, the design frameworks for key functional modules in the construction of intelligent fish farm are proposed.

/pdf/intelligent-fish-farm-the-future-of-aquaculture-2fg55s1zqe.pdf

Intelligent fish farm-the future of aquaculture.

In our banking system, banks have many products to sell but main source of income of any banks is on its credit line. So they can earn from interest of those loans which they credits.A bank's profit or a loss depends to a large extent on loans i.e. whether the customers are paying back the loan or defaulting. By predicting the loan defaulters, the bank can reduce its Non- Performing Assets. This makes the study of this phenomenon very important. Previous research in this era has shown that there are so many methods to study the problem of controlling loan default. But as the right predictions are very important for the maximization of profits, it is essential to study the nature of the different methods and their comparison. A very important approach in predictive analytics is used to study the problem of predicting loan defaulters: The Logistic regression model. The data is collected from the Kaggle for studying and prediction. Logistic Regression models have been performed and the different measures of performances are computed. The models are compared on the basis of the performance measures such as sensitivity and specificity. The final results have shown that the model produce different results. Model is marginally better because it includes variables (personal attributes of customer like age, purpose, credit history, credit amount, credit duration, etc.) other than checking account information (which shows wealth of a customer) that should be taken into account to calculate the probability of default on loan correctly. Therefore, by using a logistic regression approach, the right customers to be targeted for granting loan can be easily detected by evaluating their likelihood of default on loan. The model concludes that a bank should not only target the rich customers for granting loan but it should assess the other attributes of a customer as well which play a very important part in credit granting decisions and predicting the loan defaulters.

An Approach for Prediction of Loan Approval using Machine Learning Algorithm

Fish disease diagnosis is a difficult process and needs high level of expertise. Any attempt of developing the system dealing with the fish disease diagnosis and to overcome various difficulties is in it has still not met any extra ordinary success. Identification of diseased fish at early stage is necessary step to prevent from spreading disease. This paper recognizes and identifies the EUS (Epizootic Ulcerative syndrome) disease which is caused by bn Aphanomyces invadans, a fungal pathogen. It is a red spot disease and looks like ulcer hence it is generally misidentified by the people. The paper is divided into two parts, in the first part segmentation has been applied to enhance the image, various edge detection techniques have applied to get the useful information and Morphological operations have been applied on the EUS diseased fish image. In second part features are extracted from the EUS infected fish image through different feature Descriptors i.e. HOG (Histogram of Gradient), FAST (Features from Accelerated Segment Test) and classify the EUS infected and Non-EUS infected fish image through Machine Learning Algorithms and find the classification accuracy through Classifier. If PCA applied after feature extraction then it increases the accuracy as it is a dimensional reduction. The proposed combination of techniques gives better accuracy as compared to the others. The Experimentation has been done on MATLAB environment on real images of EUS infected fish database.

Image processing techniques for identification of fish disease

Edge detection is one of the most commonly used operations in image analysis. Several edge detectors have been proposed in literature for enhancing and detecting of edges. In this paper a new and optimal approach of edge detection based on Cellular Automata (CA) has been proposed. The idea is simple but effective technique for edge detection that greatly improves the performances of complicated images. The comparative analysis of various image edge detection methods is presented and shown that cellular automata based algorithm performs better than all these operators under almost all scenarios. General Terms: Edge Detection, Image Processing, Computation Time.

/pdf/a-novel-method-of-edge-detection-using-cellular-automata-3bl6ulfhad.pdf

A Novel Method of Edge Detection using Cellular Automata

One of the essential tasks in Digital image processing is edge detection. Edge detection generally makes the process of image segmentation and pattern recognition a bit more comfort. Edge detection significantly reduces the amount of data and filters out useless information, while preserving the relevant structural properties in an image. The paper applied the edge detection techniques on color images as well as on grey scale images. It has been shown that the Canny’s edge detection algorithm performs better than all other methods under almost all scenarios. It has been observed that canny edge detection method gives better result in gray scale images as well as in color. Moreover just to add Edge detection in color images has not received the same attention as gray scale images. The difference between color images and gray-level images is that, in a color image, a color vector (which generally consists of three components (RGB) is assigned to a pixel. Thus, in color image processing, vector-valued image functions are used instead of scalar image functions. The paper is implemented using MATLAB 12.0.

/pdf/comparative-analysis-of-edge-detection-between-gray-scale-50km2sn6wk.pdf

Comparative Analysis of Edge Detection between Gray Scale and Color Image

Real-Time identification automated system diagnoses fish disease i.e. Epizootic Ulcerative syndrome (EUS) which is caused by Aphanomyces invadans , a fungal pathogen. In this paper we propose a Real-Time fish disease diagnose system with better accuracy. In order to improve the accuracy we propose a combination (PCA-FAST-NN) which combine the  Principle component analysis (PCA) with Features from Accelerated Segment Test (FAST)feature detector using Machine Learning Algorithm(Neural Network) i.e. (PCA-FAST-NN) .The Experimentation has been done on the real images of  Epizootic Ulcerative syndrome (EUS) infected fish database and implemented in MATLAB environment.

/pdf/a-novel-approach-to-fish-disease-diagnostic-system-based-on-1uxvyx0t9a.pdf

A Novel Approach to Fish Disease Diagnostic System based on Machine Learning

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