Low Complexity Algorithm for Probability Density Estimation Applied in Big Data Analysis

Running inference algorithms on a huge quantity of data knows some perturbations and looses performance. One of Big Data aims is the design of fast inference algorithms able to extract hidden information on a big quantity of data. This paper proposes a new low complexity algorithm for probability density estimation given partial observations. In order to reduce the complexity of the algorithm, a finite numerical data support is adopted in this work and observations are classified by frequencies to reduce there number without loosing significance. By frequency classification we mean, the mapping from the space containing all observed values to a space containing each observable value associated with its observation frequency. This approach relies on Lagrange interpolation for approximating the frequencies with a polynomial function and then build the probability density function. To prove the reliability of the approach, a simulation is done and results shows the convergence of discussed parameters to the expected values. Big Data field can benefit considerably from proposed approach to achieve density estimation algorithms goal with low cost.

Computer Simulation of Power Deviation, Admittance Distance, Coherency Indices, and Dynamic Equivalents for Electric Power Systems

An Investigational Study of Energy Conservation Techniques in Hierarchical Routing Protocols in Wireless Sensor Network

In the advent of wireless networking, wireless sensor network (WSN) has been a constant target of research due to its potential data aggregation techniques in hostile environment. Even after crossing more than a decade, wireless sensor network is still more under research and development and less on commercial deployment when it comes to large scale wireless environment. Although, there are various issues exists in WSN that ranges from quality of service to security policies, it was frequently seen that root cause of majority of the issues originates from the energy that backs up the sensor motes to transmit the data to base station. The past research work has witness massive volumes of algorithms using various sophisticated technologies in order to mitigate the issues of energy problems in sensor motes, however, till date none of the prior studies has yet been standardized and hence the issues of unwanted power depletion still persist because of numerous unsolved factors. This paper is an attempt to study only the prominent techniques that has been introduced in the past for energy efficiency exclusively for hierarchical routing protocols. A brief review of some prior Swarm Intelligence (SI) techniques is also given a special focus for the similar purpose in this paper.

Intelligent Model for Manual Sorting of Plastic Wastes

The need for an automated sorting system cannot be overemphasized due to the growing needs for high throughput and accuracy. Manual sorting method may achieve good accuracy, but at the expense of throughput. This study formulates an automated plastic wastes identification model to overcome the limitation of the manual sorting method. The proposed identification model employed singleton Sugeno fuzzy model, image processing and template matching techniques as its classifier using physical properties of plastics such as power spectrum of sound, plastic average area and plastic recycling code as feature set. The model was developed and simulated in the MATLAB R2012a environment. The performance evaluation of the model was carried out using three performance metrics, namely, accuracy, precision and recall. The model obtained average accuracy of 0.88, average precision value of 0.77 and average recall value of 0.25, respectively for the plastic types (PET, HDPE, LDPE, and PP) used in this study.

Performance Analysis of Full Adder Circuit using Improved Feed through Logic

On the Impact of Dataset Characteristics on Arabic Document Classification

paper describes the impact of dataset characteristics on the results of Arabic document classification algorithms using TF-IDF representations. The experiments compared different stemmers, different categories and different training set sizes, and found that different dataset characteristics produced widely differing results, in one case attaining a remarkable 99% recall (accuracy). The use of a standard dataset would eliminate this variability and enable researchers to gain comparable knowledge from the published results.

The Alignment of Information Technology and Business Strategy in the Kuwaiti Companies

the fact that the business executives remain doubtful about the strategic value of information technology. One of the most critical issues facing organizations in Kuwait today is the alignment of information system strategy with the business goals and objectives. Only 28% of the surveyed companies in Kuwait claim that their IT objectives aligned to their business strategy which is a very low number. This paper uses a survey to investigate the alignment existence between the business objectives and IT strategy within different companies in different sectors located in Kuwait. The survey questionnaire distributed to IT managers and business executive managers in the selected companies. The suggested survey questionnaires have to do first, with the alignment exist between the IT strategy and the company Business objectives; Secondly, the barriers on strategic alignment arising in those companies. Keywordsalignment, strategic alignment, Alignment barriers, Information Technology

An Analysis on Finding the Influencing Factors of Preventing the Employment among the Educated Women using Data Mining Techniques

Hybrid Model with Super Resolution and Decision Boundary Feature Extraction and Rule based Classification of High Resolution Data

A Grey Level Fitting Mechanism based on Gompertz Function for Two Phase Flow Imaging using Electrical Capacitance Tomography Measurement Systems