International Journal of Intelligent Engineering and Systems

11

Authors: Rajganesh Nagarajan, Ramkumar Thirunavukarasu, Selvamuthukumaran Shanmugam

On demand provisioning with an acceptable pricing scheme of cloud computing attracts user to enter in to the cloud arena. In this paradigm, there are many cloud service providers involved to offer different level of services and the effectiveness of the service specification, identification of services is always a challenging one. Hence, we propose a broker based cloud computing framework for enabling the cloud user to specify their service requirements in terms of numerical representation. The infrastructure type of services such as computing and storage are considered for this proposal. With respect to the user specification, the proposed broker constructs the cloud ontology to represent the available services from the service repository. With the aid of ontological representation, the proposed broker discovers the infrastructure services from the available vendor. The appropriate services are represented using semantic network which enables the user to know about the available services as per their posted requirements. Finally, the broker recommends the services with its add-on features to the cloud users. A prototype model is developed to simulate the functioning of the broker based service discovery in the cloud and the results show the performance upgradation by comparing the traditional approach.

29

Authors: Badr Nasiri, Ahmed Errkik, Jamal Zbitou, Abdelali Tajmouati, Larbi El Abdellaoui, Mohamed Latrach

In this paper, a new microstrip band-stop filter based on a complementary split ring resonator unit cell is proposed, simulated and fabricated on an FR-4 substrate. The design procedure is divided into three sections. Firstly the conventional 3rd Butterworth low-pass prototype a cut-off frequency of 1.2GHz is designed. Secondly, several series of optimisations have been carried out and studied in order to achieve a miniature circuit size, thirdly the low impedance of the miniaturized LPF is replaced by the chosen resonator with a view to obtaining a band-stop behaviour around of CSRR resonant frequency. The simulated and measured results are compared and good agreement is achieved between them. The proposed band-stop filter is characterized by stopband fractional bandwidth of 58% with deep rejection can reach 30 dB at the centre of the rejected band and good electrical performances in the first and second passbands are noticed. This proposed circuit is an adequate solution for the Industrial, Scientific, and Medical band (ISM band) and Universal Mobile Telecommunications System (UMTS IMT-E band).

49

Authors: Ahmed Tealab, Hesham Hefny, Amr Badr

Forecasting financial market indices became a necessary operation for investors’ decisions in order to get the maximum return of investments. The complex behavior of the stock market requires development of forecasting systems. This work proposes a short-term stock fuzzy decision system using a novel trading strategy based on mixture of technical indicators. Fuzzy logic is applied for both trading rules definition and portfolio management. The selected stock market technical indicators for designing trading rules consist of commonly used indicators and new developed one. That is based on daily candlestick information produces short and long entry signals. The proposed system is tested using the Athens Stock Exchange data. The results are compared to classical non-fuzzy systems in addition to latest fuzzy approaches. The proposals performance produced less losses and better profits. The results demonstrate that the fuzzy logic is promising in portfolio management with steady upward profit and low losses. However, that deserves more study.

68

Authors: Osamah Ali Mohammed Ghaleb, Anna Saro Vijendran

One of the most widely used medium of communication over internet is social media networking where users can share and discuss their opinions. In such medium, it is highly important to track the sentiments of people to make wise decision. A combination of sentiment analysis and sentiment variation methods is used to track the variation of in public sentiments. TwitterSentiment and SentiStrength tools are two state-of-the-art sentiment analysis tools. The main aim of this paper is to improve sentiment analysis using Improved TwitterSentiment and SentiStrength in order to achieve more accurate sentiment labeling result, thus will improve tracking and interpretation of public sentiments. TwitterSentiment has been enhanced using Semantic Role Labeling (SRL), which extract semantic arguments and roles and Kullback-Leibler Divergence (KLD) for labeling the sentiments based on the extracted roles, and this is referred to as Improved TwitterSentiment. Further, the issues in SentiStrength are resolved using Natural Language Processing (NLP) technique and Maximum entropy classifier, and this is referred to as Improved SentiStrength, which aims to extracts the features of poor grammars in short informal tweets and classifying sentiments accurately. The results of both the Improved TwitterSentiment and Improved SentiStrength are then considered to label the sentiments. Thus, the performance of the sentiment analysis enhanced through the combination of Improved TwitterSentiment and Improved SentiStrength. This paper proposed three improved methods. For experimental study purposes dataset has been collected from Twitter about two key targets namely “Narendra Modi” and “Apple”. The performance of the proposed sentiment method is found to be efficient with high accuracy of 0.87, high precision of 0.89, high recall of 0.85 which has been outperformed the existing methods.

91

Authors: Abdiansah Abdiansah, Azhari Azhari, Anny Kartika Sari

Research in Textual Entailment (TE) has been widely conducted, mainly in natural language based systems, since TE can provide solutions to semantic problems. Usually, the researchers focus on method improvement, hence, they use standard data sets, which are specific to a particular language, primarily in English. For low-resource languages, it is very difficult to find data sets to test the TE systems. Therefore, in this paper we propose a model to extract data from the web to serve as data set for TE systems. The model can be used for cross-language domains with simple modifications. Two datasets are created and used to evaluate the model, i.e. DS-100-R, which contains facts, and DS-100-W, which contains non-facts. The model produces a set of sentences that are expected to be relevant to the queries. Some algorithms are created to address problems that arise during experiments. Based on the evaluation, the model accuracy for DS-100-R dataset is 79.0%, and for DS-100-W dataset is 70.0%. Hence, the overall model accuracy is 74.5%.

109

Authors: Amit Gupta, Bipin Kumar Tripathi, Vivek Srivastava

Computation intelligence is an interesting field having ability to solve many complex problems that are exist in real world. Suitable collaboration of different type of computational learning intelligence techniques viz. fuzzy, evolutionary and neural methods can be more efficient for solving real word complex problems. This paper presents comparative analysis using some intelligence approaches viz. fuzzy c-means clustering(FCM), evolutionary fuzzy clustering with Minkowski distance (EFC-MD) and fusion of EFC-MD with functional modular neural network (EFCMD-FMNN) for determine their efficiencies in real domain (first generation neural network) and complex domain (second generation neural network) both. EFC-MD is introduced for pre-classification, specifying the optimal number of cluster that are assigned in training process. Rather than the Euclidean distance, Minkowski provides the flexibility to algorithm clustering in achieving any size for the cluster keeping the distance matrix in mind. A new approach, hilbert transformation is used for converting real datasets in complex datasets (complex number form) for the computation in complex domain. In this paper initially two existing real domain datasets (wine datasets and monk datasets) is selected and complexities these datasets in complex domain using hilbert approach. Next application of various computational algorithm separately in respective domain to evaluating performance in terms of accuracy. Exploration of these algorithms in complex domain (2-D) investigates improved results and better learning characteristics in the compression of real domain (1-D).

130

Authors: Kaviya Ramalingam, Ramamurthy Bhojan

Optical Character Recognition (OCR) deals with the recognition of characters in a text document. Steps like Preprocessing, Segmentation and Recognition are embedded in the OCR machine. When a document is scanned it will be taken into OCR and will recognize the characters. But noisy scanning of documents, low-quality printed documents and thresholding error leads to the generation of broken characters. When these documents are given as inputs into OCR, the recognition becomes a tedious process since the broken characters are misunderstood by the OCR machine. So the broken characters have to be identified and segmented separately. This work aims to enhance the degraded documents with broken characters using image processing techniques. For identifying or recognizing the broken character from the image various techniques like vertical projection profile, horizontal projection profile, chain code, mean based thresholding are used. The lines from the document are separated using line segmentation. Separate characters are extracted using Vertical Projection Profile and Horizontal Projection Profile. The character is identified using chain coding. The broken characters are found from them using Mean-based Thresholding and is merged using Heuristic information. The proposed method achieves an accuracy of 92.88% and also performs well for color image documents as well as black and white image documents also because of the effective preprocessing.

149

Authors: Renaldi Primaswara Prasetya, Fitri Utaminingrum, Wayan Firdaus Mahmudy

The development of technologies that utilize eye movements as interface for motion detection has an important role in human-computer interaction especially as a medium for controlling automated device such as electrical wheelchair. The reliability of eye movement detection can determine the system performance which can be implemented into an automated device. By tracking the movement of the eyeball, communication between users and automated devices can take easily and may be used by people with hand-foot impairment. In this paper, we propose a method by using the division of the eye region and checking the eyeball midpoint position for detecting the direction of eyeball movement which can be used as a navigation in real time condition We also implement Haar Cascade method for detecting the eye region followed by tracking using Kernelized Correlation Filter to produce stable movement. This approach was proved reliable and could increase the precision and recall on eyeball movement detection significantly, reach the average to 0.93 and 0.84. Even, our proposed method enables a suitable level of sensitivity with low computational time.

169

Authors: Imane Assini, Abdelmajid Badri, Khadija Safi, Aicha Sahel, Abdennaceur Baghdad

In the aim to contribute to the security of medical image, we present a robust watermarking method which combines discrete wavelet transform (DWT), discrete cosine transform (DCT) and singular value decomposition (SVD). This approach is intended to insert an invisible image watermark in a medical image. The cover medical image is divided up into the third level of DWT coefficients and then is transformed by DCT and SVD. The same procedure is applied to the watermark image. The singular value of watermark is inserted into the singular value of the high-frequency sub bands of the third level DWT of the cover image. However, the insertion of the watermark in these areas makes it possible to reinforce the robustness of the system of watermarking without hindering the quality of the watermarked image. The performance of the proposed method was evaluated in term of invisibility by calculating the Peak Signal to Noise Ratio (PSNR) between the original and the watermarked image and in term of robustness by measuring the normalized correlation coefficient (NC) between the original watermark and the extracted watermark after applying attacks. The experimental results approve that our proposed hybrid algorithm gives an excellent compromise imperceptibility and robustness against several attacks such as Gaussian noise, Salt-and-Pepper, Speckle noise, Average filter, Median filter, and Wiener filter compared with existing methods.

186

Authors: Surendra Pandurang Ramteke, Ajay Anil Gurjar, Dhiraj Sheshrao Deshmukh

Handwritten optical character recognition (OCR) is a noteworthy research region because of its sensitivity in segmenting the character which increments on account of MARATHI script because of modifiers and compound characters. This paper gives a streamlined OCR framework for handwritten MARATHI text document classification and recognition system. To deal with a vast measure of features, the support vector machine (SVM) assumes a noteworthy part which was likewise used for the classification reason. In this paper, we display a projection profile segmentation technique which generates less error. The Curvelet Transform (CT) to be exceptionally efficient and hearty to get the feature characters from the pre-processed image. The extracted feature sets are decreased by Principle Component Analysis (PCA) algorithm. After the feature extraction process, the Adaptive Cuckoo Search (ACS) algorithm is used for the optimization procedure. Here, the written by hand MARATHI script was segmented flexibly in three levels; (1) line segmentation, (2) word segmentation and (3) character segmentation. The preprocessing was finished utilizing different morphological operations. The experimental results show that, the performance of the proposed technique is assessed in view of the accuracy, sensitivity, precision, recall and F-score. Compared with the existing Fire Fly Selection (FFS) and Bat Selection (BS) approach, the proposed method has 99.36% accuracy, 90% sensitivity, 91% precision, 89.51% recall, 99.67% specificity and 89.93% F-score. The proposed approach is actualized using MATLAB and the realtime Marathi character datasets are used for our examination.

205

Authors: Atef Tayh Raslan1* Nagy Ramadan Darwish

Software effort estimation methods are used to measure the total time and forecast the amount of required effort to develop the software. In addition, agile software projects, the requirements are characterized by changeability during the software projects. Therefore using the traditional estimation models in agile software projects may cause inaccurate effort estimation. This paper proposes a framework which utilizes The Constructive Cost Model (COCOMO II), story points, and fuzzy logic models without affecting the sacredness of agile principles. Furthermore, the results show the proposed framework increasing the value of Prediction Level (Pred) to 80%.

223

Authors: Hemant Kumar Soni, Sanjiv Sharma, Arvind Kumar Upadhyay

Support and confidence based Association rules mining algorithms have certain problems. Although other metrics like interest factor, comprehensibility, lift, correlation etc. are available to measure the interestingness of association rules. All the objectives are not suitable for each and every situation. All the objectives which were proposed in the literature, have some drawback, like correlation analysis gives equal importance to the items those are present and absent in transaction database. Resultant the rules generated by this, sometime mislead decision makers. Hence there is a strong need to define some new objectives for association rules that support in effective decision making. In this paper, authors proposed two novel objectives, high correlation and low correlation for 2-variables and 3-variables. These novel objectives clearly indicate that how much or how less two/three items are correlated. On the basis of this, decision makers can form their business strategies. An empirical algorithm for high and low correlative association rules generation is also proposed. With numeric evolution and experiments on the real-life data set, effectiveness has been measured and found that proposed algorithm gives better results.

242

Authors: Gogi Reddy Narendrababu Reddy, Singamsetty Phanikumar

Cloud computing is the development of distributed computing, parallel computing, and grid computing, or defined as a commercial implementation of such computer science concepts. One of the main issues in a cloud computing environment is Task scheduling (TS). In Cloud task scheduling, many Non deterministic Polynomial time-hard optimization problem, and many meta-heuristic (MH) algorithms have been proposed to solve it. A task scheduler should adapt its scheduling strategy to changing environment and variable tasks. This paper amends a cloud task scheduling policy based on Modified Ant Colony Optimization (MACO) algorithm. The main contribution of recommended method is to minimize makespan and to perform Multi Objective Task Scheduling (MOTS) process by assigning pheromone amount relative to corresponding virtual machine efficiency. MACO algorithm improves the performance of task scheduling by reducing makespan and degree of imbalance comparatively lower than a basic ACO algorithm by its multi-objective and deliberate nature. Experimental outcomes have shown that proposed MACO to have makespan 350 milliseconds and average utilization of 0.51 for a set of 100 tasks.

261

Authors: Sreenivas Mekala, Raghunadha Reddy Tippireddy, Vishnu Vardhan Bulusu

The rapidly growing data in the web result in stolen, unidentified and fraudulent data. Identification of such data is of a prime objective for forensic departments, researchers and governments. In this context, authorship analysis is very useful to reveal the truth by analyzing the text. Authorship analysis is observing the properties of a text to predict authorship of a document. Stylometry is the root for authorship analysis, which is a linguistic research field that exploits the machine learning techniques as well as knowledge of statistics. Authorship Attribution is a type of authorship analysis technique, which is aimed at recognizing the author of an anonymous text within a closed set of authors or subjects. Most of the researchers in Authorship Attribution approaches proposed various set of stylistic features to differentiate the authors based on style of writing. It was observed from the literature the accuracy of author prediction was not satisfactory with stylistic features. In this paper, the experimentation carried out with various stylistic features, feature selection measures and term weight measures identified in various text processing domains to predict the author of a new document. A new document representation approach is proposed to improve the prediction accuracy of author prediction. In the proposed approach the documents were represented with the weights of the documents specific to author group of documents. The results show that the proposed approach obtained good accuracies when compared with the results of stylistic features, feature section measures, term weight measures and most of the existing approaches.

280

Authors: Santhakumar Rathnasamy, Amutha Prabha Nagarajan

The paper deals with the study of throughput maximization by relay selection and assignment along with channel and subcarrier allocation. In particular, Kruskal algorithm is applied to solve radio resource allocation problem in wireless communication. Solution for joint optimization of carrier and relay is proposed in this work. Graphical theory approach is examined for solving the problem optimally in a given polynomial time. Total network throughput performances variations are analysed through numerical simulation for various combinations of transmit power nodes and relay nodes. It is also demonstrated in this paper the improvement in sum rate against Greedy’s algorithm and Prim’s algorithm using proposed Kruskal algorithm. Simulation results obtained show that the Kruskal algorithm based approach around 10% to 22% improvement in Sumrate over the fixed subcarrier pairing for a given source power for a carrier and number of relays.