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In stock market, successful investors can earn maximum profits depended on a stock selection and a suitable time on trading. Generally, investors use two statistical techniques for making a decision, which are the fundamental analysis and the technical analysis. Recently, machine learning models which are a part of artificial intelligence, has been applied to enhance investors for investment. A number...
In this paper authors propose design methodology and application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in prediction of anxiety of students using hybrid learning algorithm to improve the prediction based on the conventional model using questioner. Here, first order Sugeno fuzzy model considered whose parameters are tuned through hybrid learning algorithm. The performance of proposed model...
Coverage problem is a fundamental issue in designing efficient wireless sensor networks, in which both coverage rate and energy consumption should be considered. A brain storm optimization algorithm is a swarm intelligence algorithm which is inspired by the human brainstorming process. This paper will focus on the application of the brain storm optimization algorithm in full coverage problems of wireless...
Traveling salesman problem (TSP) which is a classic combinational optimization problem has a wide range of applications in many areas. Many researchers focus on this problem and propose several algorithms. However, it was proved to be NP-hard, which is very difficult to be solved. No algorithm can solve any types of this problem effectively. In order to propose an effective algorithm for TSP, this...
As to solve the shortcomings such as waste of resources, high cost of support, low support efficiency as a result of the over-stocked materiel and under-stocked materiel existing in equipment maintenance materiel storage support, the coordinative allocation was put forward to alleviate the problem of imbalanced resource storage, in which the over-stocked materiel was well used to satisfy the units...
This paper presents the design and implementation of a new adaptive feature selection technique for spectral band selection prior to classification of remotely sensed hyperspectral images. This approach integrates spectral band selection and hyperspectral image classification in an adaptive fashion, with the ultimate goal of improving the analysis and interpretation of hyperspectral imaging. The four...
This paper proposes a relational aggregation algorithm based on Radio Frequency Identification (RFID) to achieve accurate indoor localization. The proposed algorithm is composed of three steps: (1) exploring the relationship between reader received power and distance information then estimating Euclid distance of signal strength; (2) employing k-Nearest Neighbour algorithm to aggregate the relationship...
This paper investigates the problem of continuous observer design for single-link robot arm systems based on sampled and delayed output measurements. By constructing a Lypunov-Krasovskii function, an observer is designed for the systems, which is of continuity and hybrid. Exponential stability of the estimation errors is achieved. The upper bounds of the sampling period and the time delay are also...
Community structure is one of the most important properties for understanding the topology and function of a complex network. Recently, the rank reduction technique, non-negative matrix factorization (NMF), has been successfully used to uncover communities in complex networks. In the machine learning literature, the algorithm Alternating Constraint Least Squares (ACLS) is developed to perform NMF...
K-Winner-take-all (kWTA) is an operation that identifies the k largest inputs from multiple input signals. It has important applications in machine learning, statistics filtering and sorting, etc. As the number of inputs becomes large and the selection process should be operated in real time, parallel algorithms are desirable. For these reasons, many neural network algorithms have been proposed to...
This paper presents a two-time-scale neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems. The problem is formulated as a bi-convex optimization problem with spectral or Frobenious condition number as robustness measure. Coupled recurrent neural networks are applied for solving the formulated problem in different time scales. Simulation results of the...
Nowadays, social networks are an essential part of modern life. People posts everything what happens with them and what happens around them. The amount of data, producing by social networks, increases dramatically every year and users more often post geo-tagged messages. It gives us more possibilities for visualization and analysis of social data, since we can be interested not only in the content...
With the continued scaling down of electronic device dimensions, circuit design under parameter variations has received increasing interests. In this paper, a new method that combine the differential evolution with hybrid analysis method is presented to solve the worst-case circuit tolerance design problem. The hybrid analysis method is comprised of two commonly used worst-case circuit tolerance analysis...
This paper proposes a new multiple attribute decision making method based on the proposed interval-valued intuitionistic fuzzy weighted geometric averaging (IVIFWGA) operator, the proposed interval-valued intuitionistic fuzzy ordered weighted geometric averaging (IVIFOWGA) operator and the proposed interval-valued intuitionistic fuzzy hybrid geometric averaging (IVIFHGA) operator of interval-valued...
This paper proposes a novel autocratic decision making method using group recommendations based on ranking interval type-2 fuzzy sets. The proposed method can overcome the drawbacks of the existing group decision making methods in interval type-2 fuzzy sets environments.
Spam has been a serious and annoying problem for decades. Even though plenty of solutions have been put forward, there still remains a lot to be promoted in filtering spam emails more efficiently. Nowadays a major problem in spam filtering as well as text classification in natural language processing is the huge size of vector space due to the numerous feature terms, which is usually the cause of...
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