A concept targeting information technology is considered. The model of selection of the optimal amount of advertising on various Internet resources, in order to maximize the desired reach to the target audience is analyzed which. This model is different from traditional. A chance constrained target programming model was developed after considering the parameter that corresponds to reach for different media as random variables. The random variables in this case has been considered as the values with known mean and standard deviation. The reachability parameter can be determined by finding the ideal solution and the law on which the parameter values change. The method of multicriteria optimization is examined with determination of resulting objective function, which allows to consider various aspects of the problems of media choice and optimal budgeting and budget allocation simultaneously to get a satisfactory solution of the problem.

With the increase in load, the AC terminal voltage and frequency at the consumer premises decreases. Hence, there is encountered a need of some simple and intelligent controller which can support both voltage and frequency under load conditions. This manuscript emphasis on implementation and testing of traditional PI and non- traditional fuzzy PI logic (FPIL) based voltage and frequency controller unit (VFCU) in MATLAB based SIMULINK environment for an IIG for step change in consumer load. The electronic Controller comprises of various elements: a 3-leg diode bridge rectifier, chopper as a switch, dc filtering capacitor, discrete PI controller block, fuzzy logic controller block, and a resistive type dump load. The transient and the steady state behavior of IIG-VFCU system is examined and tested under different operating conditions to illustrate the efficaciousness of intelligent controller as compared to traditional PI controller. The performance enhancement of IIG is attained relating to rise time, settling time, overshoot and THD values using the planned intelligent controller.

Exploiting social media data by extracting key information from it is one of the great challenges in data mining and knowledge discovery. Every election campaign has an online presence of voters which uses these social media platform to express their sentiments and opinions towards political parties, leaders and important topics. This paper presents a novel data collection technique for prediction of election outcomes and a topic modeling method for extracting topics. Data collection technique used RSS (Rich Site Summary) feeds of news articles and trending keywords from Twitter simultaneously and constructed an intelligent prediction model based primarily on the volume of tweets and sentiment of users. This paper effort to improve electoral predictions using social media data based dynamic keyword methodology.
Different techniques for electoral prediction based on social media data has been investigated based on existing literature and isolate the factors which improve our methodology. Meaningful inferences such as the popularity of leaders and parties during different intervals, trending issues, and important factors are extracted from the data set. The election outcomes are compared with traditional methods used by survey agencies for exit polls and validation of results showed that social media data can predict with better accuracy. The research has identified that data collection technique and timing play an important role in yielding better accuracy in predicting outcomes and extracting meaningful inferences.

The automatic recognition of handwriting is a particularly complex operation. Until now, there is no algorithm able to recognize handwriting without that; there are assumptions to take in advance to facilitate the task of the process. A handwritten text can contain letters lowercase, uppercase letters, characters sticks and digits. Therefore, it is capital to know extract and separate all these different units in order to process them with specific algorithms to their class handwriting.
In this paper, we present a system for unconstrained handwritten text recognition, which allows to achieve this operation thanks to an intelligent segmentation based on an iterative cutting in a multi-script environment.
The results obtained from the experimental protocol reach an "equal error rate" (EER) neighboring to 6%. These calculations were calculated with a relatively small base; however this same rate can be decreased with great bases. Our results are extremely encouraging for the simple reason that our approach is situated in a more general context unlike other approaches which define several non-rigid assumptions; this clearly makes the problem simpler and may make it trivial.

The paper analyzes modern methods of modeling impacts on information systems, which made it possible to determine the most effective approaches and use them to optimize the parameters of security systems. And also as a method to optimize data security, taking in the security settings account (number of security measures, the type of security subsystems, safety resources and total cost information) allows to determine the optimal behavior in the “impact-security”. Also developed special software that allowed to verify the proposed method.

Keyphrases are set of words that reflect the main topic of interest of a document. It plays vital roles in document summarization, text mining, and retrieval of web contents. As it is closely related to a document, it reflects the contents of the document and acts as indices for a given document. Extracting the ideal keyphrases is important to understand the main contents of the document. In this work, we present a keyphrase extraction method that efficiently finds the keywords from English documents. The methods use some important features of the document such as TF, TF*IDF, GF, GF*IDF, TF*GF*IDF for the purpose. Finally, the performance of the proposal is evaluated using well-known document corpus.

Swarm intelligence algorithms (SIA) are bio-inspired techniques based on the intelligent behavior of various animals, birds, and insects. SIA are problem-independent and are efficient in solving real world complex optimization problems to arrive at the optimal solutions. Monkey behavior based algorithms are one among the SIAs first proposed in 2007. Since then, several variants such as Monkey search, Monkey algorithm, and Spider Monkey optimization algorithms have been proposed. These algorithms are based on the tree or mountain climbing and food searching behavior of monkeys either individually or in groups. They were designed with various representations, covering different behaviors of monkeys and hybridizing with the efficient operators and features of other SIAs and Genetic algorithm. They were explored for applications in several fields including bioinformatics, civil engineering, electrical engineering, networking, data mining etc. In this survey, we provide a comprehensive overview of monkey behavior based algorithms and their related literatures and discuss useful research directions to provide better insights for swarm intelligence researchers.

At present, an anomaly detection is one of the important problems in many fields. The rapid growth of data volumes requires the availability of a tool for data processing and analysis of a wide variety of data types. The methods for anomaly detection are designed to detect object’s deviations from normal behavior. However, it is difficult to select one tool for all types of anomalies due to the increasing computational complexity and the nature of the data. In this paper, an improved optimization approach for a previously known number of clusters, where a weight is assigned to each data point, is proposed. The aim of this article is to show that weighting of each data point improves the clustering solution. The experimental results on three datasets show that the proposed algorithm detects anomalies more accurately. It was compared to the k-means algorithm. The quality of the clustering result was estimated using clustering evaluation metrics. This research shows that the proposed method works better than k-means on the Australia (credit card applications) dataset according to the Purity, Mirkin and F-measure metrics, and on the heart diseases dataset according to F-measure and variation of information metric.