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The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment...
The development of smart decision support systems (DSSs) that seek to simulate human behavioral aspects is a major challenge for computational intelligence (CI). Artificial neural network (ANN) approaches have the ability to solve complex decision-making problems that involve uncertainty and a large amount of information in a fast and reliable way. The application of this evolutionary CI technique...
The interactive dynamic influence diagrams provides a way to model and solve multi-agent decision-making problems from the perspective of the subject agent. The subject agent usually optimizes its own decisions by predicting the behavior of other agents. The exponential increase in the model of other agents over time bring great difficulties to the decision. In this paper, we propose a learning algorithm...
The authors based their Research and Development Project (RDP) on a mixed research method that is mainly based on intelligent neural networks and action research reasoning approach; where both methods are very similar and resemble to the human brain structure and its way of functioning. The applied research method is founded on a real life case for detecting and processing heuristic algorithms for...
Human-Robot Interaction: to what degree can a military leader leave the robot to act on its own as opposed to the margin left for human decision-making in combat? This article covers our understanding of autonomy and the opportunity military leaders have in using autonomous military robotic systems in field operations. There is a pre-condition: all machines, including autonomous lethal robotic systems,...
The approach to the solution of the technical diagnostics problem of a transport data transmission network (TDTN) is presented, which allows to work with different groups of diagnostic data depending on the requirements by the time the task is completed, which in turn will allow reducing the average time of realization of the network diagnosis process; takes into account the physical and logical structures...
Human-level control through deep learning and deep reinforcement learning have revealed the unique and powerful potentials through a very complex Go game. The AlphaGo, developed by Google DeepMind, has beat the top Go game player early this year. The scientific and technological advancement behind the success of AlphaGo attracted researchers from multiple areas, including machine learning, artificial...
One of the most important aspects of the marketing is to determine what price is to be fixed to sell your products. Pricing is both an art and science that requires an experimental and statistical formula for creating a profile for the brand and the product in the market. There are minimalistic approaches used for pricing the products and to consider what will work for your business. Neural networks...
This paper addresses the multi-armed bandit problem in a multi-player framework. Players explore a finite set of arms with stochastic rewards, and the reward distribution of each arm is player-dependent. The goal is to find the best global arm, i.e., the one with the largest expected reward when averaged out among players. To achieve this goal, we develop a distributed variant of the well-known UCB1...
In the article developed the structure of the automated system for house devices control using machine learning algorithms. The main element of the proposed structure is responsible for setting up automated devices parameters, according to the data from sensors in the home, based on decision making using artificial neural network.
This paper provides a review of research on the application of data mining techniques for decision making in agriculture. The paper reports the application of a number of data mining techniques including artificial neural networks, Bayesian networks and support vector machines. The review has outlined a number of promising techniques that have been used to understand the relationships of various climate...
In this paper a method to develop artificial intuition is suggested. In an attempt to emulate the trial and error, searching is combined with a random choice. It is used in initial steps of the search, which provides reaching the goal in fewer steps, when compared to the case without the random choice. An example game is derived to illustrate the proposed searching technique.
This paper aims to solve the optimal power and gas flow problem of the integrated electricity and natural gas networks. Gas-fired power plants provide linkage between electricity and natural gas networks. The model of natural gas network consisting of gas sources, loads, pipelines and compressors is calculated by the Newton-Raphson method. Afterwards, a multi-objective group search optimizer with...
Transit network design problem is a typical NP-complete problem, when designing the transit network, we have to maximize the number of satisfied passengers, the total number of transfers and the total travel time of all served passengers. To some extent, it is also a multi-criteria decision-making problem. In this paper, a new model based on discrete wolf pack search algorithm (DWPS) is proposed to...
The Taiwan Bridge Management System (TBMS) has been online for 15 years and has an inventory of 33,275 bridges, including all kinds of bridges and culverts in Taiwan. Currently, the number of fields in all tables in the databases of TBMS is around 6,500 with more than 3 million data records in its databases. Meanwhile, bridge inspection data and maintenance data are increasing at a speed of 15,000...
In this paper we have proposed a system that will be able to analyze the Optimum Crop Cultivation of Bangladesh based on the knowledge of Neuro-Fuzzy System (NFS). The Neuro-fuzzy system is the collection of two techniques: fuzzy logic and the neural network. The system can compute the yield of a certain crop by using the value of humidity, temperature and rainfall. By using this system farmer will...
Fault classification in distance protection of transmission lines, with considering the wide variation in the fault operating conditions, has been very challenging task. In this paper, an approach is presented to classify the fault in transmission line based on Empirical Mode Decomposition (EMD) using instantaneous power for each phase of only one terminal. For decision making stage of proposed methodology,...
Outlier analysis is an essential task in data science to find out inconsistencies in data to build a good classifier in better decision making. Outlier's detection from categorical data is a big task. Outliers from data ought to eliminate to model a better Classifier. While modeling categorical data, infrequent records which are less than the threshold value are treated as outliers and these outliers...
Cognitive radio (CR) is a software defined radio with artificial intelligence (AI) i.e. it can learn from and adapt to ambient radio environment. Most of the research in the field of CR has been centered around policy-based systems that are hard-coded with certain rules for reasoning and learning capabilities for very specific applications. In CR networks, multiple interacting capabilities are required...
A CMAC (Cerebellar Model Articulation Controller) is a kind of feed-forward neural networks (FFNNs), but the feature of fast learning makes it different from classic FFNNs. A CMAC has a single linear trainable layer, but due to the input information is distributed in a hypercube grid, it is suitable for modeling any non-linear relationship. It has been proved that a linguistic CMAC (LCMAC) based on...
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