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30,468 Article Results

An Effective Pre-Processing Phase for Gene Expression Classification

10.11591/ijeecs.v11.i3.pp1223-1227
Choon Sen Seah , Shahreen Kasim , Mohd Farhan Md Fudzee , Mohd Saberi Mohamad , Rd Rohmat Saedudin , Rohayanti Hassan , Mohd Arfian Ismail , Rodziah Atan
A raw dataset prepared by researchers comes with a lot of information. Whether the information is usefull or not, completely depends on the requirement and purposes. In machine learning, data pre-processing is the very initial stage. It is a must to make sure the dataset is totally suitable for the requirement. In significant directed random walk (sDRW), there are three steps in data pre-processing stage. First, we remove unwanted attributes, missing value and proper arrangement, followed by normalization of the expression value and lastly, filtering method is applied. The first two steps are completed by Bioconductor package while the last step is works in sDRW.
Volume: 11
Issue: 3
Page: 1223-1227
Publish at: 2018-09-01

Kinect-Based Physiotherapy and Assessment: A Comprehensive Review

10.11591/ijeecs.v11.i3.pp1176-1187
Fadilla ‘Atyka Nor Rashid , Nor Surayahani Suriani , Ain Nazari
Kinect-based physical rehabilitation grows significantly as a mechanism for clinical assessment and rehabilitation due to its flexibility, low-cost and markerless system for human action capture. It is also an approach to provide convenience for for patients’ exercises continuation at home.  In this paper, we discuss a review of the present Kinect-based physiotherapy and assessment for rehabilitation patients to provide an outline of the state of art, limitation and issues of concern as well as suggestion for future work in this approach. The paper is constructed into three main parts. The introduction was discussed on physiotherapy exercises and the limitation of current Kinect-based applications. Next, we also discuss on Kinect Skeleton Joint and Kinect Depth Map features that being used widely nowadays. A concise summary with significant findings of each paper had been tabulate for each feature; Skeleton Joints and Depth Map. Afterwards, we assemble a quite number of classification method that being implemented for activity recognition in past few years.
Volume: 11
Issue: 3
Page: 1176-1187
Publish at: 2018-09-01

Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network

10.11591/ijeecs.v11.i3.pp1083-1093
Jeyapriya J , K S Umadevi , R Jagadeesh Kannan
The diagnosing features for Diabetic Retinopathy (DR) comprises of features occurring in and around the regions of blood vessel zone which will result into exudes, hemorrhages, microaneurysms and generation of textures on the albumen region of eye balls. In this study we presenta probabilistic convolution neural network based algorithms, utilized for the extraction of such features from the retinal images of patient’s eyeballs. The classifications proficiency of various DR systems is tabulated and examined. The majority of the reported systems are profoundly advanced regarding the analyzed fundus images is catching up to the human ophthalmologist’s characterization capacities.
Volume: 11
Issue: 3
Page: 1083-1093
Publish at: 2018-09-01

Application of Multi Layer Artificial Neural Network in the Diagnosis System: A Systematic Review

10.11591/ijai.v7.i3.pp138-142
Arvind Singh Rawat , Arti Rana , Adesh Kumar , Ashish Bagwari
Basic hardware comprehension of an artificial neural network (ANN), to a major scale depends on the proficientrealization of a distinctneuron. For hardware execution of NNs, mostly FPGA-designed reconfigurable computing systems are favorable .FPGA comprehension of ANNs through a hugeamount of neurons is mainlyan exigentassignment. This workconverses the reviews on various research articles of neural networks whose concernsfocused in execution of more than one input neuron and multilayer with or without linearity property by using FPGA. An execution technique through reserve substitution isprojected to adjust signed decimal facts. A detailed review of many research papers have been done for the proposed work.
Volume: 7
Issue: 3
Page: 138-142
Publish at: 2018-09-01

A Review of Crude Oil Prices Forecasting using Hybrid Method

10.11591/ijeecs.v11.i3.pp1114-1120
Nurull Qurraisya Nadiyya Md-Khair , Ruhaidah Samsudin
Crude oil is considered as a crucial energy source in modern days. Consequently, the fluctuation of crude oil prices can cause a significant impact on economic activities. Researchers have proposed many hybrid forecasting models on top of single forecasting methods which are utilized to predict crude oil prices movement more accurately. Nevertheless, many limitations still existed in hybrid forecasting models and models that can predict crude oil prices as accurate as possible is required. The motivations of this review paper are to identify and assess the mostly used crude oil prices forecasting methods and to analyse their current limitations. 12 studies that used “decomposition-and-ensemble” framework was selected for review. Wavelet transform is identified as the mostly used data decomposition method while some limitations have been recognized. Future researches should include more studies to further elucidate the limitations in existing forecasting method so that subsequent forecasting methods can be improved.
Volume: 11
Issue: 3
Page: 1114-1120
Publish at: 2018-09-01

Signal Processing in Telecommunications with Forward Correction of Errors

10.11591/ijeecs.v11.i3.pp868-877
Juliy Boiko , Oleksander Eromenko
The development of mechanisms of increase efficiency of frequency-shift keying signals processing in telecommunications using algorithms of noise immunity channel coding in obstacle effect conditions is held in the article. The synthesis of the frequency-shift keying signal processing unit accounting intersymbol communication which is inherent for such signals with continuous phase is held. The conditions of the compromise implementation in the telecommunication information transmission channel with frequency shift keying and error correction coding for setting the optimal encoding rate in the range of the bandwidth of the information transmission system are explored. Linear cyclic codes Bose-Chaudhuri-Hocquenghem (BCH) are used for studying. By means of Matlab the article focuses on the definition of energetic benefit compared to uncoded system in case of equality of the bandwidth of the information transmission system with coding and without coding.
Volume: 11
Issue: 3
Page: 868-877
Publish at: 2018-09-01

Cat Swarm Optimization to Shunt Capacitor Allocation in Algerian Radial Distribution Power System

10.11591/ijai.v7.i3.pp143-152
Amar Hamzi , Rachide Meziane
This paper presents a Cat Swarm Optimization (CSO) Algorithm optimization method to shunt capacitor placement on distribution systems under capacitor switching constraints. The optimum capacitor allocation solution is found for the system of feeders fed through their transformer and not for any individual feeder. The main advantages due to capacitor installation, such as capacity release and reduction of overall power and energy losses are considered. The capacitor allocation constraints due to capacitor-switching transients are taken into account. These constraints are extremely important if pole-mounted capacitors are used together with station capacitor bank. Cat Swarm search algorithm is used as an optimization tool. An illustrative example for Algerian example is presented.
Volume: 7
Issue: 3
Page: 143-152
Publish at: 2018-09-01

Homotopy Analysis Method for the First Order Fuzzy Volterra-Fredholm Integro-differential Equations

10.11591/ijeecs.v11.i3.pp857-867
Ahmed A. Hamoud , Kirtiwant P. Ghadle
A fuzzy Volterra-Fredholm integro-differential equation (FVFIDE) in a parametric case is converted to its related crisp case.  We use homotopy analysis method to find the approximate solution of this system and hence obtain an approximation for the fuzzy solution of the  FVFIDE. This paper discusses existence and uniqueness results and convergence of the proposed method.
Volume: 11
Issue: 3
Page: 857-867
Publish at: 2018-09-01

The Fuzzy Inference System with Least Square Optimization for Time Series Forecasting

10.11591/ijeecs.v11.i3.pp1015-1026
Samingun Handoyo , Marji Marji
The rule base on the fuzzy inference system (FIS) has a major role since the output generated by the system is highly dependent on it. The rule base is usually obtained from an expert but in this study proposed the rule base generated based on input-output data pairs with generating rule bases using lookup table scheme, then consequent part of each rule optimized with ordinary least square(OLS), so finally formed rule base from model FIS Takagi-Sugeno orde zero. The exchange rate dataset of EURO to USD is used for the development and validation of the system. In this study, 12 FISs were developed from a combination of linguistic values of n = 3,5,7, 9 with the number of lag (k) assumed to have an effect on output for k = 2,3,5. In training data, values R2 ranged between 0.989 and 0.993, MAPE values ranged between 0.381% and 0.473% where the FIS with the combination of n = 9 and k = 5 has the best performance. In the testing data, values R2 ranged between 0.203 and 0.7858, MAPE values ranged between 0.5136% and 0.9457% where FIS n = 3 and k = 2 perform best.
Volume: 11
Issue: 3
Page: 1015-1026
Publish at: 2018-09-01

A Novel Approach for Efficient Training of Deep Neural Networks

10.11591/ijeecs.v11.i3.pp954-961
D.T.V. Dharmajee Rao , K.V. Ramana
Deep Neural Network training algorithms consumes long training time, especially when the number of hidden layers and nodes is large. Matrix multiplication is the key operation carried out at every node of each layer for several hundreds of thousands of times during the training of Deep Neural Network. Blocking is a well-proven optimization technique to improve the performance of matrix multiplication. Blocked Matrix multiplication algorithms can easily be parallelized to accelerate the performance further. This paper proposes a novel approach of implementing Parallel Blocked Matrix multiplication algorithms to reduce the long training time. The proposed approach was implemented using a parallel programming model OpenMP with collapse() clause for the multiplication of input and weight matrices of Backpropagation and Boltzmann Machine Algorithms for training Deep Neural Network and tested on multi-core processor system. Experimental results showed that the proposed approach achieved approximately two times speedup than classic algorithms.
Volume: 11
Issue: 3
Page: 954-961
Publish at: 2018-09-01

Modified SHA-1 Algorithm

10.11591/ijeecs.v11.i3.pp1027-1034
Rogel Ladia Quilala , Ariel M Sison , Ruji P Medina
Hashes are used to check the integrity of data. This paper modified SHA-1 by incorporating mixing method in every round for better diffusion. The modification increased the hash output to 192-bits. Increasing the output increases the strength because breaking the hash takes longer. Based on the different message types, avalanche percentage of modified SHA-1 showed better diffusion at 51.64%, higher than the target 50%, while SHA-1 achieved 46.61%. The average execution time noted for modified SHA-1 is 0.33 seconds while SHA-1 is 0.08 seconds. Time increases as the number of messages hashed increases; the difference is negligible in fewer messages. On character hits, that is - no same character in the same position, modified SHA-1 achieved lower hit rate because of the mixing method added. The modifications’ effectiveness was also evaluated using a hash test program. After inputting 1000 hashes from random strings, the result shows no duplicate hash.
Volume: 11
Issue: 3
Page: 1027-1034
Publish at: 2018-09-01

Distribution Factor Method Modified for Determine of Load Contribute based on the Power Factor in Transmission Line

10.11591/ijeecs.v11.i3.pp1236-1242
Syarifuddin Nojeng , Syamsir Syamsir , Arif Jaya , Andi Syarifuddin , Mohammad Yusri Hassan
This paper proposes a modification of distribution factor methods for identifying the load contribute in a transmission open access, with regard to the load power factor. This method may be considered as the first pricing strategy to be proposed in bilateral transaction for transmission usage, based on the actual use of the transmission network. The merit of this method relies on the existence of a load power factor with GLDF  methods, which allocate the  transmission cost, not only based on the amount of power flow but also on the load characteristic. A case study utilizing the IEEE 30-bus system was conducted to illustrate the contribution of the proposed method in allocating the transmission usage to the user in a fair manner.
Volume: 11
Issue: 3
Page: 1236-1242
Publish at: 2018-09-01

Matchmaking Problems in MOBA Games

10.11591/ijeecs.v11.i3.pp908-917
Muhammad Farrel Pramono , Kevin Renalda , Dedy Prasetya Kristiadi , Harco Leslie Hendric Spits Warnars , Worapan Kusakunniran
MOBA is a popular genre that requires teamwork to achieve victory. A close and tight match is what make MOBA fun to play and increase its user satisfaction, but some factor may ruin the matchmaking and create unbalanced match between the two team. Those problems factors are high latency, players with bad attitude, and players doing unfamiliar role. We use DOTA 2 as our case study. Then we compare the DOTA 2 matchmaking system in other sector to make comparison. Lastly, we discuss about solution to solve MOBA matchmaking problem such as displaying live information about online players, players searching for games, servers online and ETA for gaming to start. In addition, we proposed new variable to be considered in the matchmaking system, which are Preferences Role, player’s chosen preferences role will be considered while the system set up the game to minimize the number of unbalanced games in MOBA.
Volume: 11
Issue: 3
Page: 908-917
Publish at: 2018-09-01

Analysing Event-Related Sentiments on Social Media with Neural Networks

10.11591/ijai.v7.i3.pp119-124
P. Santhi Priya , T. Venkate swara Rao
Sentiment analysis is performed to determine the polarity of opinion on a subject. It has been applied to text corpora such as movie reviews, financial documents to glean information about overall-sentiment anc produce actionable data. Recent events have demonstrated that polling can be sometimes unreliable. People can be difficult to access through conventional polling methods and less than frank in polls. In the era of social media, voters are likely to more freely express their opinion on social media forums about divisive events especially in media where anonymity exists. Analyzing the prevailing opinion on these forums can indicate if there are any deficiencies in polling and can be a valuable addition to conventional polling. We analyzed text corpora from Reddit forums discussing the recent referendum in Britain to exit from the EU (known as Brexit). Brexit was an important world event and was very divisive in the run-up and post vote. We analyzed sentiment in two ways: Initially we tried to gauge positive, negative, and neutral sentiments. In the second analysis, we further split these sentiments into six different polarities based on the directionality of the positive and negative sentiments (for or against Brexit). Our technique utlilized paragraph vectors (Doc2Vec) to construct feature vectors for sentiment analysis with a Multilayer Perceptron classifier. We found that the second analysis yielded overall better results; although, our classifier didn’t perform as well in classifying positive sentiments. We demonstrate that it is possible glean valuable information from complicated and diverse corpora such as multi-paragraph comments from reddit with sentiment analysis.
Volume: 7
Issue: 3
Page: 119-124
Publish at: 2018-09-01

Cascaded Neural Network Based Data Mining Strategy for Cloud Intrusion Detection

10.11591/ijeecs.v11.i3.pp1094-1101
P Purniemaa , R Jagadeesh Kannan
In recent years data mining has acquired huge popularity in the field of knowledge discovery. Thus, this approach has inspired several researches for anomaly detection, fraud detection and intrusion detection with higher accuracy, all round generalization of the problem and its sub cases; all giving higher performance in conditions subjected to continuous alteration. Though there remain quite a few challenging problems in design and implementation of a data mining based cloud intrusion detection system, as deception tactics and modeling of behavior remains a daunting problem to compute for anomaly owing to massive size of data to process in reasonable time. In this study we present a cascaded neural network based data mining strategy for cloud intrusion detection systems (IDSs) and presents the comparison and performance results tested on DARPA Intrusion Detection (ID) Data Sets, Knowledge Discovery and Data Mining Cup, NSL-KDD dataset. The study exhibits numerous advantages offered by the presented method and give reliable results of anomaly detection in real time scenario.
Volume: 11
Issue: 3
Page: 1094-1101
Publish at: 2018-09-01
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