Optimization of fuzzy analogy in software cost estimation using linguistic variables

One of the most important objectives of software engineering community has been the increase of useful models that beneficially explain the development of life cycle and precisely calculate the effort of software cost estimation. In analogy concept, there is deficiency in handling the datasets containing categorical variables though there are innumerable methods to estimate the cost. Due to the nature of software engineering domain, generally project attributes are often measured in terms of linguistic values such as very low, low, high and very high. The imprecise nature of such value represents the uncertainty and vagueness in their elucidation. However, there is no efficient method that can directly deal with the categorical variables and tolerate such imprecision and uncertainty without taking the classical intervals and numeric value approaches. In this paper, a new approach for optimization based on fuzzy logic, linguistic quantifiers and analogy based reasoning is proposed to improve the performance of the effort in software project when they are described in either numerical or categorical data. The performance of this proposed method exemplifies a pragmatic validation based on the historical NASA dataset. The results were analyzed using the prediction criterion and indicates that the proposed method can produce more explainable results than other machine learning methods.Comment: 14 pages, 8 figures; Journal of Systems and Software, 2011. arXiv admin note: text overlap with arXiv:1112.3877 by other author

RESTORATION OF MEMORY AND ACETYLCHOLINESTERASE ACTIVITY BY MICHELIA CHAMPACA IN CHRONICALLY NOISE STRESSED WISTAR ALBINO RATS

ABSTRACTObjective:The ability of an organism to adapt to aversive stressful situations or life challenging circumstances is very crucial to its state of health and survival. However, breakdown in adaptation due to persistent uncontrollable stress, leads to impairment of bodily functions and onset of a variety of pathological disorders especially memory decline. This study was designed to evaluate the effect of Michelia champaca(M.champaca) a potent antioxidant on chronic noise stress induced memory impairment in rats. Methods: Male wistar albino rats were used in this study. Animals were exposed to noise for 30 consecutive days (4hrs/day) before testing for memory. Thereafter, the plasma corticosterone level and acetylcholinesterase activity were estimated in the three discrete regions of the brain homogenate using spectrophotometer. Result:Our results showed that M.champaca prevented memory impairment and suppressed corticosterone concentrations induced by chronic noise stress. Moreover it also decreased brain acetylcholinesterase activity when compared with chronic stress group (p < 0.05). Conclusions:These findings suggest that M.champaca attenuates memory deficits induced by chronic noise stress in albino rats and may be useful therapeutically for stress-related cognitive dysfunctions. The reduction in the levels of serum corticosterone and inhibition of cholinesterase enzyme might be contributing significantly to the positive effect of M.champaca on memory in rats exposed to chronic noise stress.Keywords: M.champaca, memory, corticosterone, chronic noise stress, acetylcholinesterase activity, Eight-arm radial maze

Intuitionistic Partition based Conceptual Granulation Topic-Term Modeling

Document Analysis represented in vector space model is often used in information retrieval, topic analysis, and automatic classification. However, it hardly deals with fuzzy information and decision-making problems. To account this, Intuitionistic partition based cosine similarity measure between topic/terms and correlation between document/topic are proposed for evaluation. Conceptual granulation is emphasized in the decision matrix expressed conventionally as tf-idf. A local clustering of topic-terms and document-topics results in comparing dependent terms with membership degree using cosine similarity measure and correlation. A preprocessing of documents with intuitionistic fuzzy sets results in efficient classification of large corpus. But it depends on the datasets chosen. The proposed method effectively works well with large sized categorized corpus

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics

Neural Network based p-q-r Theory for Harmonic Reduction and Neutral Current Mitigation

The power quality compensator chosen in this paper is a DSTATCOM which integrates a three phase four leg Voltage Source Converter (VSC) with a DC capacitor. The major role of the DSTATCOM is to mitigate the components of harmonic/reactive current present in the line current thereby shapes the grid current to be sinusoidal and improves the power factor nearly unity under varying conditions. In addition DSATATCOM mitigates neutral current (Isn) and balances the load currents under unbalanced conditions in three phase four wire (3P4W) distribution system. The control strategy proposed for the DSTATCOM is a Neural Network (NN) based p-q-r theory with two Artificial Neural Network (ANN) controllers for a 3P4W distribution system. The reference signal for 3P3W Shunt Active Power Filter (SAPF) is calculated by implementing an ANN controller. The alleviation of Isn under unbalanced condition is achieved by another ANN controller which produces reference signal for the 1Φ APF. The performance of the proposed DSTATCOM is analysed for various conditions through simulations in MATLAB SIMULINK and the simulation results justify the effectiveness of the propounded NN based control algorithm for DSTATCOM

Improved power quality buck boost converter for SMPS

In this paper, a Neural Network (NN) controlled Buck-Boost Converter (BBC) based Switched Mode Power Supply (SMPS) for a PC application is proposed. The proposed BBC is analyzed, modeled and designed for the rated load. Generally, the utilization of Multiple Output SMPS (MOSMPS) for PC application introduces Power Quality (PQ) issues in the power system network. Unlike conventional SMPS the proposed NN controlled BBC can accomplish improvement of power quality. The NN controller reduces the Total Harmonic Distortion (THD) of source current below 5%, maintains input side Power Factor (PF) to be nearly unity and improves the output voltage regulation. In the proposed system, NN controller replaces the conventional PI controller and overcomes the drawbacks of the conventional system. The proposed BBC is validated adopting MATLAB/SIMULINK software. The simulation analysis validate that the proposed NN controlled BBC performs better than conventional converter in terms of PQ indices under fluctuating conditions

An Efficient Image Denoising Approach for the Recovery of Impulse Noise

Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image's pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics

ROLE OF MICHELIA CHAMPACA IN MEMORY ENHANCEMENT AND ACUTE NOISE STRESSED MALE WISTAR ALBINO RATS

Objective: To identify the memory enhancing role of Michelia champaca in acute noise stressed animals. Methods: Male Wistar albino rats were used in this study. Animals were exposed to noise for 4 h before testing for memory. Thereafter, the plasma corticosterone level and acetylcholinesterase activity were estimated in the discrete regions of the brain, and the memory related behavior were assessed by eight arm radial maze.Results: Our results showed that Michelia champaca enhances the memory activity and decreases the corticosterone concentrations in acute noise stress animals treated with M. champaca. Moreover, it also decreased brain acetylcholinesterase activity when compared with the acute stress group (p<0.05). Furthermore, behavioral tests indicate that working memory, is enhanced by acute stress and decreases the error levels in all the parameters studied in the behavior aspects when compared to control animals.Conclusion: These findings suggest that Michelia champaca enhances the memory in albino rats and might be useful therapeutically for cognitive related dysfunctions. This could be due to the presence of memory boosting compounds and its antistressor and anti-acetylcholinesterase activity, thereby reduces the levels of serum corticosterone and inhibition of cholinesterase enzyme significantly

Analysis of system capacity and spectral efficiency of fixed-grid network

In this article, the performance of a fixed grid network is examined for various modulation formats to estimate the system's capacity and spectral efficiency. The optical In-phase Quadrature Modulator structure is used to build a fixed grid network modulation, and the homodyne detection approach is used for the receiver. Data multiplexing is accomplished using the Polarization Division Multiplexed technology. 100 Gbps, 150 Gbps, and 200 Gbps data rates are transmitted under these circumstances utilizing various modulation formats. Various pre-processing and signal recovery steps are explained by using modern digital signal processing systems. The achieved spectrum efficiencies for PM-QPSK, PM-8 QAM, and PM-16 QAM, respectively, were 2, 3, and 4 bits/s/Hz. Different modulation like PM-QPSK, PM-8-QAM, and PM-16-QAM each has system capacities of 8-9, 12-13.5, and 16-18 Tbps and it reaches transmission distances of 3000, 1300, and 700 kilometers with acceptable Bit Error Rate less than equal to 2*10-3 respectively. Peak optical power for received signal detection and full width at half maximum is noted for the different modulations under a fixed grind network