bluuebunny/crossref_metadata_2025 huggingface dataset:
A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease
Authors: Wiharto Wiharto , Herianto Herianto , Kusnanto Hari
Published: January 2019

The use of dimensional reduction in the diagnostic system model of coronary heart disease, many same of case do not take into account the clinical procedures commonly used by clinicians in diagnosis. This requires that the examination be done thoroughly, thus making the high cost of diagnosis. This study aims to develop a tiered approach model in reducing dimensions for predicting CHD. The method in this research is divided into several stages, namely preprocessing, building the knowledge base and system testing. Preprocessing consists of several processes, namely the removal of missing value data, grouping attributes, and dividing data for training and testing. Knowledge base modeling is divided into three levels. The first level were the risk factor attributes, the second level were the type of chest pain & ECG, and the third were scintigraphy & coronary angiography. The knowledge base was modeled based on fuzzy rules and its inferencing process using Mamdani method. The first, fuzzy rule-based was obtained by using the FRS study. The second and third stage, using the induction rule algorithm to get the rule, then converted to fuzzy rule. The tested algorithm were C4.5, CART, and FDT. The system testing was performed by the 5-folds cross-validation method, with performance parameters based on population and individual. The test resulted using the Cleveland and Hungarian datasets, the FRS+CART combination was capable of reducing the most attributes and the highest likelihood ratio performance parameter, which was 15.96. FRS+C4.5, at least the attributes were reduced, but has an AUC performance of 80.43%, while FRS+FDT, more reduced attributes than FRS+C4.5, and AUC performance parameters are better than FRS+CART. Dimensional reduction model for prediction of CHD, capable of providing better performance than not tiered.
An Efficient Schema of a Special Permutation Inside of Each Pixel of an Image for its Encryption
Authors: Ali-Pacha Hana , Hadj-Said Naima , Ali-Pacha Adda , Mamat Mustafa , Mohamed Mohamad Afendee
Published: January 2019

The developments of communications and digital transmissions have pushed the data encryption to grow quickly to protect the information, against any hacking or digital plagiarisms. Many encryption algorithms are available on the Internet, but it's still illegal to use a number of them. Therefore, the search for new the encryption algorithms is still current. In this work, we will provide a preprocessing of the securisation of the data, which will significantly enhance the crypto-systems. Firstly, we divide the pixel into two blocks of 4 bits, a left block that contains the most significant bit and another a right block which contains the least significant bits and to permute them mutually. Then make another permutation for each of group. This pretreatment is very effective, it is fast and is easy to implement and, only consumes little resource.
RF Simulations for AAβ Cryptosystem, an Asymmetric Encryption Scheme
Authors: Adnan Syed Farid Syed , Isa Mohd Anuar Mat , Hashim Habibah
Published: January 2019

Internet of Things (IoT) is a way of providing data with the physical thing that interconnected to the network, which is the Internet. The IoT devices connected to the internet, broadcast of the data to the broker or a server, becomes an open route for attackers to gain the data and making the data becomes vulnerable. Thus, the data could be altered or spoofed by an attacker which led to security issues especially on data integrity. Therefore, the data security collected from the sensors is as important as on the servers that eventually become the big data. However, most sensors are low powered devices in term of CPU, storage, memory and batteries that cryptographic algorithm computations might give overhead to the sensors and reduce the batteries even faster than it is supposed to be. Instead of looking at symmetric encryption scheme, this paper tries to explore the capabilities of the asymmetric scheme on resource constrained devices communications. Thus, this paper presents an RF communication analysis of a low consumption asymmetric encryption, the AAβ (AA-Beta) that promising to implement on the IoT devices to secure the IoT networks. The result shows only 14% size increased in ciphertext from plaintext and the RF simulation communications show a better result in Raspbian OS environment compare to windows environment even though with same configurations
Evaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification
Authors: Sabri Nurbaity , Ibrahim Zaidah , Isa Dino
Published: January 2019

This paper investigates the application of eight color models for automatic palm oil Fresh Fruit Bunch (FFB) ripeness classification with multi-class Support Vector Machine (SVM).  Ripeness classification is important during harvesting to ensure that they are harvested during the correct ripe stage for optimum oil production.  Since color is a significant indicator for agriculturists to determine the ripeness of FFB, it is critical to determine the right color model. Eight color models have been investigated namely, HSV, I1I2I3, LAB, XYZ, YCbCr, YIQ, YUV and RGB. Color moments were extracted from each of these color models for the classification of four stages of FFB ripeness that are unripe, under-ripe, ripe and over-ripe.  A database of five hundred images of palm oil FFB has been constructed and experiments showed that YCbCr and YUV outperform the other color models.
Business Intelligence for Paintball Tournament Matchmaking Using Particle Swarm Optimization
Authors: Mishan M.T. , Fadzil A.F.A. , Samah K.A.F.A. , Baharin N.F. , Anuar N.
Published: January 2019

Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.
Mixed Integer Linear Programming for Maintenance Scheduling in Power System Planning
Authors: Hussin S.M. , Hassan M.Y. , Wu L. , Abdullah M.P. , Rosmin N. , Ahmad M.A.
Published: January 2019

This paper discussed the merit of mixed-integer linear programming (MILP)-based approach against Lagrangian relaxation (LR)-based approach in solving generation and transmission maintenance scheduling problem. MILP provides a straightforward solution by formulating coupling constraints equations so that these sub-problems can be solved simultaneously without involving multipliers. In LR-based approach, generation and transmission maintenance scheduling, and security-constrained unit commitment have been solved individually and the integration was realized through a series of multipliers which has caused computational burden to the system. Numerical case studies were evaluated on the 6-bus system. A comparative study is carried out between the MILP and LR approaches. Simulation results indicate that the maintenance schedule derived by the proposed MILP approach outperforms the LR in terms of operational cost savings and gap tolerance. The operating cost could be saved up to 5% and the gap tolerance achieved is 0.01% as compared to 0.14% by LR.
A Micro-Scale Cyclone-Wind Turbine for Rooftop Ventilator
Authors: Wibawa Unggul , Permadi Akhmad Frandicahya , Hasanah Rini Nur
Published: January 2019

This paper analyzes the model of a cyclone-turbine for a micro-scale wind-power system being motivated by an idea to harvest the abandoned energy from rooftop ventilators. The system under consideration has been equipped with a battery to form a wind-battery power system. Data obtained from a wind site observation have been used to calculate the potentially generated power and efficiency, as well as the mechanical and electrical designs to extract the energy. The design has been explored to obtain the best efficiency of the cyclone-turbine model. The impact of wind-speed variation on the resulted system output has been investigated during the charging process of battery. The conclusion emphasizes the relationship between the output power and the range values of the resulted current and voltage, as well as the optimum wind speed-range of the cyclone-turbine operation.
Mobile Application for Electric Power Monitoring on Energy Consumptions at a Campus University
Authors: Kassim Murizah , Rahman Maisarah Abdul , Yahya Cik Ku Haroswati Che Ku , Idris Azlina
Published: January 2019

This paper presents a research on electric power monitoring prototype mobile applications development on energy consumptions in a university campus. Electric power energy consumptions always are the issue of monitoring usage especially in a broad environment. University campus faces high used of electric power, thus crucial analysis on cause of the usage is needed. This research aims to analyses electric power usage in a university campus where implemented of few smart meters is installed to monitor five main buildings in a campus university. A Monitoring system is established in collecting electric power usage from the smart meters. Data from the smart meter then is analyzed based on energy consume on 5 buildings. Results presents graph on the power energy consume and presented on mobile applications using Live Code coding. The methodology involved the setup of the smart meters, monitoring and data collected from main smart meters, analyzed electrical consumptions for 5 buildings and mobile system development to monitor. A Live Code mobile app is designed then data collected from smart meter using ION software is published in graphs. Results presents the energy consumed for 5 building during day and night. Details on maximum and minimum energy consumption presented that show load of energy used in the campus. Result present Tower 1 saved most eenergy at night which is 65% compared to block 3 which is 8% saved energy although block 3 presents the lowest energy consumption in the working hours and non-working hours. This project is significant that can help campus facility to monitor electric power used thus able to control possible results in future implementations.
Microinverter Topology based Single-stage Grid-connected Photovoltaic System: A Review
Authors: Razi A. , Hidayat M. Nabil , Seroji M. N.
Published: January 2019

This paper discussed the topology development of a single-stage microinverter in grid-connected PV system. In general, the microinverter topologies can be categorized into four type of topologies:          1) Flyback inverter, 2) Double-boost inverter, 3) Derived zeta-cuk configuration and 4) Buck-boost inverter. Flyback configuration is widely used for single-stage microinverter which offers protection between solar panel and utility grid. However due to the bulkiness of the transformer, new arrangement circuit employ the Half-Bridge topology with film capacitor and microcontroller provide a good room for research and future developments to obtain greater efficiency and compact design of single-stage microinverter grid-connected PV system. Plus, there are several characteristics need to be taken care for future development of the microinverter technology.
SIW Circular Cavity Single Mode Filter With Triangle Probe
Authors: Nordin Siti Aminah , Salleh Mohd Khairul Mohd , Khan Zuhani Ismail , Wahab Norfishah Ab , Noh Latifah , Yusoff Zakiah Mohd
Published: January 2019

A novel substrate integrated waveguide (SIW) circular cavity using triangle probe are proposed in this paper. Prior to this research work, circular cavity resonator was used to achieve a miniaturization for the overall circuit size. The proposed filter provides single resonant mode, TE110. The resonant frequency of TE110 can be adjusted by varying the length and width of the SIW cavity. The proposed filter are designed to operate at frequency 3.75 GHz and implemented on Rogers 3210 substrate with thickness of 0.64 mm. The insertion loss in operating band is less than 0.6 dB and the return loss is better than 24 dB. Simulated result obtained using Ansoft HFSS software.
Comparison of Chemical Compounds of Essential Oils from Natural Agarwood and Inoculated Agarwood (Roselle-Based Inoculation)
Authors: Nasardin N.R.M. , Hanafiah M.A.M. , Zainon M. , Ibrahim M. , Rahman A.I.A. , Baharudin Z.A. , Husin M.H.M. , Mahir I. , Zulkefle A.A.
Published: January 2019

In this paper, the physical and structural properties of the natural agarwood and inoculated agarwood using Roselle-based inoculation had been discussed. The results showed that a high volume of oil obtained on day 1 by the inoculated agarwood which yielded 3ml of the agarwood oil as compared to the natural agarwood that had yielded only 2.3ml. In the chemical analysis, five major compounds were detected by FTIR which were C-O bond (aromatic alkane), C-C stretching (aromatic alkane), C-O stretching (ether) and C-H bond (aromatic ring) in both oils. Moreover, the inoculated agarwood exhibited better oil production compared to the natural agarwood.
Assessment Analytic Theoretical Framework Based on Learners’ Continuous Learning Improvement
Authors: Hamiz M. , Bakri M. , Kamaruddin Norhaslinda , Mohamed Azlinah
Published: January 2019

Currently, university students are required to follow stringent curriculum structure regardless of their performance. Personalized learning is not being offered resulting the whole cohort must compy to a customized fixed curriculum design. This is because the designed curriculum does not take into account different students’ attainment. Furthermore, there is a mismatched between supply and demand of graduates’ skill sets to fulfil the requirement of industry. Due to these issues, employers face difficulties in finding suitable high-skilled worker which contributes to large number of unemployed graduates. Thus, a systematic intervention of students’ learning process is essential to construct informed and strategic responses in order to manage challenges and minimize skill mismatch, at the same time providing adequate fundamental knowledge. In this paper, an assessment analytics framework is proposed based on automated extracted skill sets from curriculum documents and individual performance to recommend adaptive learners’ learning system (ALLS). By preparing the graduates with the required industry skill sets, the graduates’ unemployment rate is envisaged to reduce.
Modeling and Simulation of Nine-Level Cascaded H-Bridge Multilevel Inverter
Authors: Zulkefle A.A. , Rahman A.I.A. , Zainon M. , Baharudin Z.A. , Zakaria Z. , Farriz M.B. , Shukor S.A.A. , Johan N.F. , Saleh N. , Hanafiah M.A.M.
Published: January 2019

This paper analyzed a single phase nine level cascaded h-bridge multilevel inverter (CHMLI) topology in which requires lesser number of components and easier to control if compared to other methods. The inverter of different voltage levels (up to 9-Levels) are designed and simulated using MATLAB Simulink. A Level Shifted Multi-Carrier PWM control scheme is used to control the operation of the power switches for CHMLI topology. The results are compared in terms of THD.  The value of THD is found to be reduced as the number of voltage level of CHMLI is increased.
TicTad: A Chatterbot for Learning Visual C# Programming based on Expert System
Authors: Kasinathan Vinothini , Mustapha Aida , Siow Sonia , Hopman Marshella
Published: January 2019

This paper presents TicTad, a Chatterbot on Visual C# Programming to help students who struggle with learning programming, or who wants to learn Visual C#. TicTad provides an efficacious way to memorize the concepts through an interactive gateway. This will decrease the time and effort needed to learn Visual C# as compared to traditional methods.
Opinion Mining of Light Rail Transit Development in Indonesia
Authors: Raflesia Sarifah Putri , Lestarini Dinda , Firdaus Firdaus , Rodiah Desty
Published: January 2019

Light rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development.
The Fuzzy Inference System with Least Square Optimization for Time Series Forecasting
Authors: Handoyo Samingun , Marji Marji
Published: January 2019

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.
Study of Load Optimization and Performance Issues in Cloud
Authors: Banu Madhina D , Aranganathan Aranganathan
Published: January 2019

“Nowadays, cloud computing is the latest computing platform which is feasible to the user for computation and unlimited storage and data transmission with minimal cost and time in a cloud environment during the internet. The load balancing is important criteria of cloud environment that avoid same nodes overloaded and others are idle. Ultimately load balancing can enhance the QoS parameters including make span, cost and resource utilization. To optimize the load, the existing load optimization approach is properly utilized federation mechanisms, which offers physical resources based on demand to maintain the cloud application efficiency. However, the technique failed to optimize load where part of the servers suffering from heavy load after an execution of the application. In the current scenario, several systems are facing same kinds of problem which is the biggest cause to increase the virtual machine (VM) cost. Current systems still have a time delay, request-response process error from data center side in cloud environments. To overcome these issues, the research study studies all related technique for cloud performance optimization and load balancing issues. The main framework objective is to offer an effective solution to store/search/transmit/ data with minimal cost and time without compromising the QoS constraints. The study represents different policies and cloud-specific strategies to enhance the performance of cloud application with minimal cost and time. The research study is also planning to find out an effective solution for traffic, data congestion and media streaming issues in a cloud environment.
Key Escrow with Elliptic Curve Cryptography – Conceptual Framework for Distributed Mobile Networks
Authors: Sugumar B. , Ramakrishnan M.
Published: January 2019

In the large scale distributive environment involving mobile network, metadata server and storage applications, the data access and the security measures are of paramount importance. In parallel application processing, data is distributed across multiple servers and storage location. Ensuring confidentiality and availability of the data to the authorised users at the appropriate time involves high level of encryption algorithms, key management schemes and security algorithms. In this paper, key escrow scheme is implemented with the light weighted symmetric algorithm, elliptic curve cryptography in the distributed environment. Key escrow centre is established along with the metadata server and the encryption keys are segmented and shared among the multiple sub agents using Shamir threshold sharing scheme. The implementation of Key Escrow mechanism with Elliptical Curve Cryptography provides wide range of flexibility and confidentiality in the distributed environment. It also eliminates private secret sub key problem and thereby ensuring better security.
Automated Detection of Microaneurysmsusing Probabilistic Cascaded Neural Network
Authors: J Jeyapriya , Umadevi K S , Kannan R Jagadeesh
Published: January 2019

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.
Cube Arithmetic: Improving Euler Method for Ordinary Differential Equation using Cube Mean
Authors: Samsudin Nooraida , Yusop Nurhafizah Moziyana Mohd , Fahmy Syahrul , Mokhtar Anis Shahida Niza Binti
Published: January 2019

The Euler method is a first-order numerical procedure for solving Ordinary Differential Equation (ODEs) problems. It is an effective and easy method to solve initial value problems. Although Euler provides simple procedure for solving ODEs, there have been issues such as complexity, time of processing and accuracy that compelled the use of other, more complex, methods. Improvements to the Euler method have attracted much attention resulting in numerous modified Euler methods. This paper proposes Cube Arithmetic, a modified Euler method with improved accuracy. The efficiency of Cube Arithmetic was compared with Euler Arithmetic and tested using SCILAB against exact solutions. Results indicate that not only Cube Arithmetic provided solutions that are similar to exact solutions at small step size, but also at higher step size, hence producing more accurate results.
A Review of Crude Oil Prices Forecasting using Hybrid Method
Authors: Md-Khair Nurull Qurraisya Nadiyya , Samsudin Ruhaidah
Published: January 2019

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.
Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting
Authors: Hitam Nor Azizah , Ismail Amelia Ritahani
Published: January 2019

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.
Forecasting Drought Using Modified Empirical Wavelet Transform-ARIMA with Fuzzy C-Means Clustering
Authors: Shaari Muhammad Akram , Samsudin Ruhaidah , Ilman Ani Shabri
Published: January 2019

Drought forecasting is important in preparing for drought and its mitigation plan. This study focuses on the investigating the performance of Auto Regressive Integrated Moving Average (ARIMA) and Empirical Wavelet Transform (EWT)-ARIMA based on clustering analysis in forecasting drought using Standard Precipitation Index (SPI). Daily rainfall data from Arau, Perlis from 1956 to 2008 was used in this study. SPI data of 3, 6, 9, 12 and 24 months were then calculated using the rainfall data. EWT is employed to decompose the time series into several finite modes. The EWT is used to create Intrinsic Mode Functions (IMF) which are used to create ARIMA models. Fuzzy c-means clustering is used on the instantaneous frequency given by Hilbert Transform of the IMF to create several clusters. The objective of this study is to compare the effectiveness of the methods in accurately forecasting drought in Arau, Malaysia. It was found that the proposed model performed better compared to ARIMA and EWT-ARIMA.
Identification of Rainfall Patterns on Hydrological Simulation Using Robust Principal Component Analysis
Authors: Shaharudin S.M. , Ahmad N. , Zainuddin N.H. , Mohamed N.S.
Published: January 2019

A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and  compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components .The results showed a  more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality.
Kinect-Based Physiotherapy and Assessment: A Comprehensive Review
Authors: Rashid Fadilla ‘Atyka Nor , Suriani Nor Surayahani , Nazari Ain
Published: January 2019

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.
Global Convergence of a New Coefficient Nonlinear Conjugate Gradient Method
Authors: Mohamed Nur Syarafina , Mamat Mustafa , Rivaie Mohd , Shaharuddin Shazlyn Milleana
Published: January 2019

Nonlinear conjugate gradient (CG) methods are widely used in optimization field due to its efficiency for solving a large scale unconstrained optimization problems. Many studies and modifications have been developed in order to improve the method. The method is known to possess sufficient descend condition and its global convergence properties under strong Wolfe-Powell search direction. In this paper, the new coefficient of CG method is presented. The global convergence and sufficient descend properties of the new coefficient are established by using strong Wolfe-Powell line search direction. Results show that the new coefficient is able to globally converge under certain assumptions and theories.
Enhancing Similarity Distances Using Mandatory and Optional for Early Fault Detection
Authors: Razak Safwan Abd , Isa Mohd Adham , Jawawi Dayang N.A
Published: January 2019

Software Product Line (SPL) describes procedures, techniques, and tools in software engineering by using a common method of production for producing a group of software systems that identical from a shared set of software assets. In SPL, the similarity-based prioritization can resemble combinatorial interaction testing in scalable and efficient way by choosing and prioritize configurations that most dissimilar. However, the similarity distances in SPL still not so much cover the basic detail of feature models which are the notations. Plus, the configurations always have been prioritized based on domain knowledge but not much attention has been paid to feature model notations. In this paper, we proposed the usage of mandatory and optional notations for similarity distances. The objective is to improve the average percentage of faults detected (APFD). We investigate four different distances and make modifications on the distances to increase APFD value. These modifications are the inclusion of mandatory and optional notations with the similarity distances. The results are the APFD values for all the similarity distances including the original and modified similarity distances. Overall, the results shown that by subtracting the optional notation value can increase the APFD by 3.71% from the original similarity distance.
An Efficient Approach to Detecting Missing Tags in RFID Data Stream
Authors: Zainudin Nur’Aifaa , Mahdin Hairulnizam , Witarsyah Deden , Makhtar Mokhairi , Ninggal Mohd Izuan Hafez , Baharum Zirawani
Published: January 2019

RFID technology is a Radio frequency identification system that provides a reader reading the data item from its tag. Nowadays, RFID system has rapidly become more common in our life because of its autonomous advantages compared to the traditional barcode. It can detect hundreds of tagged items automatically at a time. However, in RFID, missing tag detection can occur due to signal collisions and interferences. It will cause the system to report incorrect tag’s count due to an incorrect number of tags being detected. The consequences of this problem can be enormous to business, as it will cause incorrect business decisions to be made. Thus, a Missing Tag Detection Algorithm (MTDA) is proposed to solve the missing tag detection problem. There are many other existing approaches has been proposed including Window Sub-range Transition Detection (WSTD), Efficient Missing-Tag Detection Protocol (EMD) and Multi-hashing based Missing Tag Identification (MMTI) protocol. The result from experiments shows that our proposed approach performs better than the other in terms of execution time and reliability.
A Study of Some Iterative Methods for Solving Fuzzy Volterra-Fredholm Integral Equations
Authors: Hamoud Ahmed A. , Azeez Ali Dhurgham , Ghadle Kirtiwant P.
Published: January 2019

This paper mainly focuses on the recent advances in the some approximated methods for solving fuzzy Volterra-Fredholm integral equations, namely, Adomian decomposition method, variational iteration method and homotopy analysis method. We converted fuzzy Volterra-Fredholm integral equation to a system of Volterra-Fredholm integral equation in crisp case. The approximated methods using to find the approximate solutions of this system and hence obtain an approximation for the fuzzy solution of the fuzzy Volterra-Fredholm integral equation. To assess the accuracy of each method, algorithms with Mathematica 6 according is used. Also, some numerical examples are included to demonstrate the validity and applicability

of the proposed techniques.
This paper mainly focuses on the recent advances in the some approximated methods for solvingfuzzy Volterra-Fredholm integral equations, namely, Adomian decomposition method, variational iterationmethod and homotopy analysis method. We converted fuzzy Volterra-Fredholm integral equation to asystem of Volterra-Fredholm integral equation in crisp case. The approximated methods using to find theapproximate solutions of this system and hence obtain an approximation for the fuzzy solution of the fuzzyVolterra-Fredholm integral equation. To assess the accuracy of each method, algorithms with Mathematica 6according is used. Also, some numerical examples are included to demonstrate the validity and applicabilityof the proposed techniques.
Dynamic Voltage Restorer for Sag and Swell Issues in Power System
Authors: Lakshmi Kumari Lakshmi , Uma Vani Uma Vani
Published: January 2019

This paper presents the application of dynamic voltage restorers (DVR) on power distribution Systems for mitigation of voltage sags/swells at critical loads. DVR is one of the compensating types of custom power devices. The power quality is affected mainly due to the sensitive loads which results in voltage sag and voltage swells. It is necessary to investigate the suitable methods for mitigation of voltage sags. Sensitivity is the main cause of the above power quality problems and it cannot be eliminated completely as it has many other operating properties. So the next possible solution is to correct the problems caused by the sensitive equipments connected to the faulty loads. The occurrence of sag and swell varies with equipment, environment, process operations, desired control schemes etc. From the wide range of mitigation methods, the selected one has to be observed for the effect on the characteristics. These problems can be mitigated with voltage injection method using custom power device, Dynamic Voltage Restorer (DVR). In this paper we design a Dynamic Voltage Restorer (DVR) which is utilized for power quality improvement. The main power quality problems like voltage sag and swell are studied in this paper. The device used to phase out voltage sags and a swell in the distribution lines is the Dynamic Voltage Restorer (DVR). The Dynamic Voltage Restorer is a special type of power device used for providing consistent and reliable supply power to the load devices. Dynamic Voltage Restorer uses a vector control strategy for mitigating power quality problems by automatically detecting and injecting the voltage components through an injection transformer. Here comes the importance of soft computing techniques like PI controller. The system will be able to correct repeated occurrences of the power quality problems. 
GWO Based Optimal Reactive Power Coordination of DFIG, ULTC and Capacitors
Authors: Sankaraiah Mogaligunta , Suresh Reddy Sanna , Vijaya Kumar M
Published: January 2019

Wind is available with free of cost anywhere in the world, this wind can be used for power generation due to many advantages. This attracts the researchers to work on wind power plants. The presence of wind power plants on distribution system causes major influence on voltage controlled devices (VCDs) in terms of life of the devices. Therefore, this paper proposes grey wolf optimization method (GWO) together with forecasted load one day in advance. VCDs are on load tap changer (ULTC) and capacitors (CS), there are two main objectives first one is curtail of distribution network (DN) loss and second one is curtailing of ULTC and CS switching’s. Objectives are achieved by controlling the reactive power of DFIG in coordination with VCDs. The proposed method is planned and applied in Matlab/Simulink on 10KV practical system with DFIG located at different locations. To validate the efficacy of GWO, results are compared with conventional and dynamic programming methods without profane grid circumstances.
A Comparative Study and Analysis on Conventional Solar PV Based DC-DC Converters and MPPT Techniques
Authors: Manoharan Premkumar , K Karthick , R Sowmya
Published: January 2019

As electricity demand escalated with supply, though there are lot of thermal power station, nuclear energy and other conventional power sources. Yet, there is exhaustion in the above assets and adding dangerous impacts to the atmospheric conditions.  The world searches for sustainable power source that it is normally accessible such as sun and wind. Apart from all the renewable energy resources, solar energy is readily harnessed for domestic application to meet demand. To increase the power conversion efficiency from the solar PV system it is better have a perfect DC to DC converters. The proposed outcome of this paper is to outline the DC to DC converter with MPPT algorithms to concentrate on extreme productivity at roof-top for solar PV application which decreases the cost of energy. In addition to that it also prevents panel miss matching at all environmental conditions for safer DC Voltage with flexible site design especially for domestic applications from the solar photovoltaic module. It is necessary to analyze the converters and MPPT algorithms under closed loop condition for the design and installation of solar PV system to the load or to the grid. This review summarizes few DC to DC converter topologies, maximum power point tracking algorithm and also paid attention on the advantages and disadvantages of these algorithms and topologies.
Long Reach and High Capacity Hybrid Passive Optical Network
Authors: N Subhashini , A Brintha Therese
Published: January 2019

A number of applications are growing day by day and so the traffic. The need for bandwidth is also increasing at a rapid rate. The bandwidth and speed with which data can be transferred was very less when compared to core networks. The access network which was once a bottleneck is no longer so because of use of optic fiber (FTTH networks). A number of variants of Passive Optical Network (PON) have been proposed like the WDM PON and the Hybrid PON. Hybrid PON is a combination of TDM PON and WDM PON and is advantageous over WDMPON. This paper focuses on high capacity networks that can provide high data rate and long reach in the access part of the network. NRZ modulation format is normally used for transmission.  We consider the advantages provided by the advanced modulation formats like DPSK. This modulation format is used to here and its benefits are evaluated in Hybrid PON network to increase the capacity and the reach of the network. Parameters like the BER and the Q factors are analysed using Optisystem Software. Distortion and the phenomena of dispersion can limit the performance of such a system. Hence Dispersion compensation mechanisms like the Dispersion Compensation Fiber (DCF) are used in the system to transmit data over large distance.
Matchmaking Problems in MOBA Games
Authors: Pramono Muhammad Farrel , Renalda Kevin , Kristiadi Dedy Prasetya , Spits Warnars Harco Leslie Hendric , Kusakunniran Worapan
Published: January 2019

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.
Modified AES for Text and Image Encryption
Authors: Gamido Heidilyn V. , Sison Ariel M. , Medina Ruji P.
Published: January 2019

Advanced Encryption Standard (AES) is one of the most frequently used encryption algorithms. In the study, the Advanced Encryption Standard is modified to address its high computational requirement due to the complex mathematical operations in MixColumns Transformation making the encryption process slow. The modified AES used Bit Permutation to replace the MixColumns Transformation in AES since bit permutation is easy to implement and it does not have any complex mathematical computation. Results of the study show that the modified AES algorithm exhibited increased efficiency due to the faster encryption time and reduced CPU usage. The modified AES algorithm also yielded higher avalanche effect which improved the performance of the algorithm.
Location of Eyes in Images of Human Faces through Analysis Variance Shine Intensity
Authors: Alhamzawi Hussein Ali
Published: January 2019

Extraction of facial features is an important step in automatic visual interpretation and recognition of human faces. Among the facial features, the eyes play a major role in the recognition process. In this article, we present an approach to detect and locate the eyes in frontal face images. Eye regions are identified using the technique of voucher detection based on mathematical morphology. After this identification is made a comparison between the variances of three different portions of each candidate region to eye (set of pixels belonging to the candidate region as a whole, set of pixels contained in a minimum rectangle circumscribed to the candidate region and set of pixels of the candidate region belonging to a horizontal band that crosses the center of mass of this region). The calculation of these variances also considers the R, G, and B channels, as well as the gray version of the input image.
A Novel Approach for Efficient Training of Deep Neural Networks
Authors: Dharmajee Rao D.T.V. , Ramana K.V.
Published: January 2019

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.
Weighting Model for Group Decision Support System: A Review
Authors: Hamdani Hamdani , Wardoyo Retantyo , Mustofa Khabib
Published: January 2019

This paper was conducted a survey of research findings related to decision-making in groups using weighting techniques This paper conducts a survey of research findings related to decision-making in groups using weighting techniques, therefore, a new weighting model can be proposed.. In order to make a decision, this model works based on the weighting parameters, criteria or decision makers (DM) to give ranking preference in the decision results. Weighting could be done objectively or subjectively by statistical calculations. Subjective weighting based on an understanding or expertise of the decision-making process, furthermore, a numerical value was needed to make it objective. The previous weighting models need to be modified for the development of group decision support systems (GDSS). It is required to accommodate the interests of all stakeholders to link the DM relationship. We formed several groups of weighting methods that currently use for the trend of group decision-making (GDM). It showed that the classical multi-criteria decision-making (MCDM) models are still dominant in solving GDSS problems, therefore, it was necessary to apply hybrid MCDM with an approach method of stakeholders models and social networks (SN) to improve decision model that has been applied in the previous research.
A Survey of Video Based Action Recognition in Sports
Authors: Rahmad Nur Azmina , As'ari Muhammad Amir , Ghazali Nurul Fathiah , Shahar Norazman , Sufri Nur Anis Jasmin
Published: January 2019

Sport performance analysis which is crucial in sport practice is used to improve the performance of athletes during the games. Many studies and investigation have been done in detecting different movements of player for notational analysis using either sensor based or video based modality. Recently, vision based modality has become the research interest due to the vast development of video transmission online. There are tremendous experimental studies have been done using vision based modality in sport but only a few review study has been done previously. Hence, we provide a review study on the video based technique to recognize sport action toward establishing the automated notational analysis system. The paper will be organized into four parts. Firstly, we provide an overview of the current existing technologies of the video based sports intelligence systems. Secondly, we review the framework of action recognition in all fields before we further discuss the implementation of deep learning in vision based modality for sport actions. Finally, the paper summarizes the further trend and research direction in action recognition for sports using video approach. We believed that this review study would be very beneficial in providing a complete overview on video based action recognition in sports.