bluuebunny/crossref_metadata_2025 huggingface dataset:
Dashboard of intelligent transportation system (ITS) using mobile agents strategy on notification authentication process
Authors: Allali Najia , Chaouch Zineb , Tamali Mohammed
Published: April 2019

Extracting accurate information from huge Transportation Database need to build efficiency Intelligent Transportation Systems ITS-Dashboard that should allow making correct decisions. The quality of decision and the achievement of performance depend on the quality of the information supplied. This information must be reliable, complete, pertinent and more to care about external attacks. Distributed Mobile Agent consists of autonomy of entities with capacities of perception, cooperation and action on their own environment. One of Agent function is the security of Authentication process by activation of notification system on Mobile Device. The main purpose of this paper is to make it consisting of an Agent Based Framework. The strategy is to exploit Mobile Agent capabilities in a Strict Notification Process when user validates his authentication request.
A review on various optimization techniques of resource provisioning in cloud computing
Authors: Sumalatha K. , Anbarasi M. S.
Published: April 2019

Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet.It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning.
A-OLSR: ANFIS based OLSR to select Multi point relay
Authors: Sharma Vivek , Alam Bashir , Doja M. N.
Published: April 2019

The characteristics like dynamic topology, power consumption, mobility etc. may leads to affect the routing process of packet as it progresses from one node to another node. The energy of each node is very limited in MANET’s due to which it becomes an important parameter to be considered while selecting the route. The ‘Optimized Link State Routing Protocol’ (OLSR) does not consider node energy during Multipoint relay (MPR) selection process. This paper proposes an improvement of OLSR routing protocol named as A-OLSR protocol using node energy during its MPR process. The improvement is based on adaptive neuro fuzzy inference system (ANFIS). The network simulator NS2.35 is used for the simulation, random way point model for mobility and constant bit rate (CBR) for traffic process. The performance of proposed A-OLSR protocol is evaluated using the packet delivery ratio (PDR) and end to end delay metrices. The simulation results prove the superiority of the proposed protocol in terms of PDR.
The effect of training set size in authorship attribution: application on short arabic texts
Authors: Al-Sarem Mohammed , Emara Abdel-Hamid
Published: April 2019

Authorship attribution (AA) is a subfield of linguistics analysis, aiming to identify the original author among a set of candidate authors. Several research papers were published and several methods and models were developed for many languages. However, the number of related works for Arabic is limited. Moreover, investigating the impact of short words length and training set size is not well addressed. To the best of our knowledge, no published works or researches, in this direction or even in other languages, are available. Therefore, we propose to investigate this effect, taking into account different stylomatric combination. The Mahalanobis distance (MD), Linear Regression (LR), and Multilayer Perceptron (MP) are selected as AA classifiers. During the experiment, the training dataset size is increased and the accuracy of the classifiers is recorded. The results are quite interesting and show different classifiers behaviours. Combining word-based stylomatric features with n-grams provides the best accuracy reached in average 93%.
Implementation of a frequency control in a biomass gasifier system
Authors: Muñoz Yecid , Ospino Adalberto , Robles Carlos , Arizmendi Carlos
Published: April 2019

Distributed power generation has grown in popularity in recent years, especially in areas not connected to the national grid. As a result, rural microgrids are becoming more common, involving great potential for energy based on biomass conversion such as gasification. After analyzing distributed power generation facilities in developing countries, the authors found problems with the frequency stability. This paper focuses on solving the problem of frequency control in energy supplied by microgrids based in biomass gasification. For that purpose, the authors have developed a physical model of a downdraft gasifier, this model was used for design a novel strategy for frequency control, which has been based and validated on an existing gasification system, which supplies power to a population in Necoclí (Colombia).
Failure prediction of e-banking application system using adaptive neuro fuzzy inference system (ANFIS)
Authors: Abdillah Yuwono , Suharjito Suharjito
Published: April 2019

Problems often faced by IT operation unit is the difficulty in determining the cause of the failure of an incident such as slowing access to the internet banking url, non-functioning of some features of m-banking or even the cessation of the entire e-banking service. The proposed method to modify ANFIS with Fuzzy C-Means Clustering (FCM) approach is applied to detect four typical kinds of faults that may happen in the e-banking system, which are application response times, transaction per second, server utilization and network performance. Input data is obtained from the e-banking monitoring results throughout 2017 that become data training and data testing. The study shows that an ANFIS modeling with FCM optimized input has a RMSE 0.006 and  increased accuracy by 1.27% compared to ANFIS without FCM optimization.
Obtrusiveness of smartphone applications for sleep health
Authors: Vazquez-Briseno M. , Laflor-Hernandez A. , Murillo-Muñoz Fernanda , Mendez-Verdugo Doreen
Published: April 2019

Unobtrusiveness is one of the main issues concerning health-related systems. Many developers affirm that their systems do not burden users; however, this is not always achieved. This article evaluates the obtrusiveness of various systems developed to improve sleep quality. The systems analyzed are related to sleep hygiene, since it has become an interesting topic for researchers, physicians and people in general, mainly because it has become part of the methods used to estimate a persons’ health status A set of design elements are presented as keys to achieving unobtrusiveness. We propose a scale to measure the level of unobtrusiveness and use it to evaluate several systems, with a focus on smartphone applications. 
Enhanced model for ergonomic evaluation of information systems: application to scientific research information system
Authors: Benmoussa Khaoula , Laaziri Majida , Khoulji Samira , Mohamed Larbi Kerkeb , Yamami Abir El
Published: April 2019

Considering the importance of the ergonomic aspect for information systems in providing an easy use of the computing systems, this paper focuses on the elaboration of a hybrid model for the ergonomic evaluation of information systems that merges a set of inspection dimensions such as accessibility, practicality, emotionality, and technological persuasion. Details on how the model has been constructed and data have been collected are presented. For the evaluation of our model, a situation of an information system for scientific research (SIMArech) in Moroccan Universities has been studied. This model can be used by ergonomist and human factor professionals. It is found that the proposed heuristics can effectively assess the most important dimensions of scientific research information systems.
A new model for the selection of web development frameworks: application to PHP frameworks
Authors: Benmoussa Khaoula , Laaziri Majida , Khoulji Samira , Larbi Kerkeb Mohamed , Yamami Abir El
Published: April 2019

The use of a framework is often essential for medium and large scale developments, but is also of interest for small developments. PHP has evolved as the scripting language the most chosen by developers, which has generated an explosion of PHP frameworks. There is a big debate about what the best PHP frameworks are, because the simple fact is that not all frameworks are built for everyone. Indeed, not all frameworks meet the same needs, and several frameworks can be used together in certain situations. Choosing the right framework, however, can sometimes be difficult. In order to make the selection process easier, we propose a pragmatic and complete model to compare and evaluate the main PHP frameworks. This model is based on a set of comparison criteria based on the Intrinsic durability, industrialized solution, technical adaptability, strategy, technical architecture and Speed criteria. Results show that the values of these criteria allow developers to easily and properly choose the framwork that best meets their needs
A comparative study of laravel and symfony PHP frameworks
Authors: Laaziri Majida , Benmoussa Khaoula , Khoulji Samira , Mohamed Larbi Kerkeb , Yamami Abir El
Published: April 2019

With the current explosion of Information Systems, the market offers a wide range of interesting technological solutions. Yet, this does not mean adopting a technology without considering its impact on the existing information system and user expectations. It is recommended to identify and implement the technological solutions most suited to the Information Systems strategy. Therefore, new methods are emerging and design tools are still evolving; the PHP Frameworks are part of it, which open up new perspectives in terms of information system enrichment. In this context, this paper focuses on the elaboration of a comparative study between Laravel, symfony framworks, which are the most popular PHP frameworks.  Thus, it provides an effective comparison model that merges seven dimensions: Features, Multilingual, System requirements, Technical architecture, Code Organization, Continuous Integration (CI) and finally Documentation and learning curve dimension. Results show that our model can be beneficial for IT project developers to select the suitable PHP Framework.

 
Analyzing bootsrap and foundation font-end frameworks : a comparative study
Authors: Laaziri Majida , Benmoussa Khaoula , Khoulji Samira , Mohamed Larbi Kerkeb , Yamami Abir El
Published: April 2019

Most modern web applications use some kind of front-end frameworks for designing and creating content in a faster and more efficient way, which saves valuable time when creating responsive web sites. There are many front-end frameworks that vary enormously in terms of features and benefits, which could make the choice of front-end framework for the developer tricky. In this context, this paper focuses on an effective analysis of two of today's most popular front-end frameworks, Boostrap and Foundation, The results show that our analysis can be beneficial for developers to select the appropriate front end framework to customize their web applications.
A modified backward/forward sweep-based method for reconfiguration of unbalanced distribution networks
Authors: Duran-Quintero Michel , Candelo John E. , Soto-Ortiz Jose
Published: April 2019

A three-phase unbalanced power flow method can provide a more realistic scenario of how distribution networks operate. The backward/forward sweep-based power flow method (BF-PF) has been used for many years as an important computational tool to solve the power flow for unbalanced and radial power systems. However, some of the few available research tools produce many errors when they are used for network reconfiguration because the topology changesafter multiple switch actions and the nodes are disorganized continually. This paper presents a modifiedBF-PF for three-phase unbalanced radial distribution networks that is capable of arranging the system topology when reconfiguration changes the branch connections. A binary search is used to determine the connections between nodes, allowing the algorithm to avoid those problems when reconfiguration is carried out, regardless of node numbers. Tests are made to verify the usefulness of the proposed algorithm in both the IEEE 13-node test feeder and the 123-node test feeder, converging in every run where constraints are accomplished. This approach can be used easily for a large-scale feeder network reconfiguration. The full version of this modified backward/forward sweep algorithm is available for research at MathWorks.
Proposed algorithm for image classification using regression-based pre-processing and recognition models
Authors: Jittawiriyanukoon Chanintorn
Published: April 2019

Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics.
Noise reduction in ECG signals for bio-telemetry
Authors: Jagannaveen V. , Krishna K. Murali , Rajeswari K. Raja
Published: April 2019

In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and   Mean Square Error.
Music fingerprinting based on bhattacharya distance for song and cover song recognition
Authors: Sarno Riyanarto , Wijaya Dedy Rahman , Mahardika Muhammad Nezar
Published: April 2019

People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.
A hybrid bacterial foraging and modified particle swarm optimization for model order reduction
Authors: Abdullah Hadeel N.
Published: April 2019

This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.
Bandwidth enhancement and miniaturization of circular-shaped microstrip antenna based on beleved half-cut structure for MIMO 2x2 application
Authors: Firmansyah Teguh , Praptodiyono Supriyanto , Herudin Herudin , Aribowo Didik , Alam Syah , Astuti Dian Widi , Yunus Muchamad
Published: April 2019

In this paper, circular-shaped microstrip antenna was simulated, fabricated, and measured accordingly. As the novelty, to enhance bandwidth and reduce antenna size, beleved half-cut microstrip structure is proposed. Further, this proposed antenna structure will be applied to multiple input multiple output (MIMO) antenna 2´2. Therefore, this research was investigated conventional circular shape antenna (CCSA), circular shaped beleved antenna (CSBA), and MIMO circular shaped beleved antenna (MIMO-CBSA) as Model 1, Model 2, and Model 3, respectively. An FR4 substrate with er= 4.4, thickness h=1.6 mm, and tan d=0.0265 was used. The simulation has been conducted using Advanced Design System (ADS). The antenna CCSA/CSBA/ MIMO-CBSA achieve 1.831GHz/2.265 GHz/2.256 GHz, -15.13dB/-17.37dB/-17.25 dB, 1.42/1.31/1.33, and 1.474/2.332/2.322 for center frequency, reflection coefficient, VSWR, and bandwidth, respectively. This antenna has a size 63x90 mm and 51.5x90 mm for CCSA (Model 1) and CSBA (Model 2), respectively. After the structure of MIMO 2´2 was applied, the size of antenna MIMO-CBSA (Model 3) became 180 mm x 180 mm with a mutual coupling (S21)=-26.18 dB and mutual coupling (S31)=-26.41 dB. The result showed that proposed antenna CSBA (Model 2) has wider-bandwidth of 58,2% and smaller-size of 18.2%. Furthermore, after CSBA (Model 2) structure was applied to MIMO 2´2 (Model 3) and the MIMO antenna obtain good mutual coupling (<-15dB). Moreover, the measured results are good agreement with the simulated results. In conclusion, all of these advantages make it particularly valuable in multistandard antenna applications design such as GSM950, WCDMA1800, LTE2300, and WLAN2400.
Performance analysis of IEEE 802.11ac based WLAN in wireless communication systems
Authors: Yonis A. Z.
Published: April 2019

IEEE 802.11ac based wireless local area network (WLAN) is emerging WiFi standard at 5 GHz, it is new gigabit-per-second standard providing premium services. IEEE 802.11ac accomplishes its crude speed increment by pushing on three distinct measurements firstly is more channel holding, expanded from a maximum of 80 MHz up to 160 MHz modes. Secondly, the denser modulation, now using 256-QAM, it has the ability to increase the data rates up to 7 Gbps using an 8×8 multiple input multiple output (MIMO). Finally, it provides high resolution for both narrow and medium bandwidth channels. This work presents a study to improve the performance of IEEE 802.11ac based WLAN system.
An intelligent approach to take care of mother and baby health
Authors: Uddin Mohammad Nasser , Alam Mohammad Jahangir , Mustafa Md. Nurul
Published: April 2019

This is the era of technology and is widely used in every sector. In Bangladesh the use of technology is increasing day by day in many sectors. Health sector is one of them. This research is designed and developed to help the pregnant women to get weekly information on development and conditions of their health and the growing child inside their womb. This system will notify expectant mothers automatically   about their health checkup date and time. It provides general and special health information to the expectant mothers. It is designed with user friendly interface so that an expectant mother can use this system very effectively. This system allows a unique secure login system and provides a unique suggestion to the expectant mothers.This system is very user friendly and useful.
Energy distribution in formant bands for arabic vowels
Authors: Farchi Mohamed , Tahiry Karim , Mounir Soufyane , Mounir Badia , Mouhsen Ahmed
Published: April 2019

The acoustic cues play a major role in speech segmentation phase; the extraction of these indexes facilitates the characterization of the speech signal. In this work, we aim to study Arabic vowels (/a/, /a:/, /i/, /i:/, /u/ and /u:/), especially the long ones. We are interested in characterizing this type of vowels in terms of time, frequency and energy. The cues extracted and analyzed in this work are: segment length, voicing degree and formants values.
Genetic-fuzzy based load balanced protocol for WSNs
Authors: Kashyap Pankaj Kumar , Kumar Sushil
Published: April 2019

Recent advancement in wireless sensor networks primarily depends upon energy constraint. Clustering is the most effective energy-efficient technique to provide robust, fault-tolerant and also enhance network lifetime and coverage. Selection of optimal number of cluster heads and balancing the load of cluster heads are most challenging issues. Evolutionary based approach and soft computing approach are best suitable for counter the above problems rather than mathematical approach. In this paper we propose hybrid technique where Genetic algorithm is used for the selection of optimal number of cluster heads and their fitness value of chromosome to give optimal number of cluster head and minimizing the energy consumption is provided with the help of fuzzy logic approach. Finally cluster heads uses multi-hop routing based on A*(A-star) algorithm to send aggregated data to base station which additionally balance the load. Comparative study among LEACH, CHEF, LEACH-ERE, GAEEP shows that our proposed algorithm outperform in the area of total energy consumption with various rounds and network lifetime, number of node alive versus rounds and packet delivery or packet drop ratio over the rounds, also able to balances the load at cluster head.
MECC scheduling algorithm in vehicular environment for uplink transmission in LTE networks
Authors: Ismail Shafinaz , Ali Darmawaty Mohd , Yusof Azita Laily
Published: April 2019

Single Carrier Frequency Division Multiple Access (SC-FDMA) is chosen because of the lower peak-to-average power ratio (PAPR) value in uplink transmission. However, the contiguity constraint is one of the major constraint presents in uplink packet scheduling, where all RBs allocated to a single UE must be contiguous in the frequency-domain within each time slot to maintain its single carrier. This paper proposed an uplink-scheduling algorithm namely the Maximum Expansion with Contiguity Constraints (MECC) algorithm, which supports both the RT and NRT services. The MECC algorithm is deployed in two stages. In the first stage, the RBs are allocated fairly among the UEs. The second stage allocates the RBs with the highest metric value and expands the allocation on both sides of the matrix, M with respect to the contiguity constraint. The performance of the MECC algorithm was observed in terms of throughput, fairness, delay, and Packet Loss Ratio (PLR) for VoIP, video and best effort flows. The MECC scheduling algorithm is compared to other algorithms namely the Round Robin (RR), Channel-Dependent First Maximum Expansion (CD-FME), and Proportional Fairness First Maximum Expansion (PF-FME). From here, it can be concluded that the MECC algorithm shows the best results among other algorithms by delivering the highest throughput which is up to 81.29% and 90.04% than CD-FME and RR scheduler for RT and NRT traffic respectively, having low PLR and delay which is up to 93.92% and 56.22% of improvement than CD-FME for the RT traffic flow. The MECC also has a satisfactory level of fairness for the cell-edge users in a vehicular environment of LTE network.
Elastic neural network method for load prediction in cloud computing grid
Authors: Qaddoum Kefaya S. , El Emam Nameer N. , Abualhaj Mosleh A.
Published: April 2019

Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.
ICI and PAPR enhancement in MIMO-OFDM system using RNS coding
Authors: Youssef M. I. , Emam A. E. , Khalifa M. Abd
Published: April 2019

The Inter-Carrier-Interference (ICI) is considered a bottleneck in the utilization of Multiple-Input-Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, due to the sensitivity of the OFDM towards frequency offsets which lead to loss of orthogonality, interference and performance degradation. In this paper Residue Numbers as a coding scheme is impeded in MIMO-OFDM systems, where the ICI levels is measured and evaluated with respect to conventional ICI mitigation techniques implemented in MIMO-OFDM. The Carrier-to-Interference Ratio (CIR), the system Bit-Error-Rate (BER) and the Complementary Cumulative Distribution Function (CCDF) for MIMO-OFDM system with Residue Number System (RNS) coding are analyzed and evaluated. The results had demonstrated a performance of transmission model with and without RNS.
IACR: an interference-aware channel reservation for wireless sensor networks
Authors: Lim Sangsoon
Published: April 2019

In battery-based wireless sensor networks, energy-efficient operation is one of the most important factors. Especially, in order to improve energy efficiency in wireless sensor networks, various studies on low power operation have been actively conducted in the MAC layer. In recent years, mutual interference among various radio technologies using the same radio frequency band has become a serious problem. Wi-Fi, ZigBee, and Bluetooth use the same frequency band of 2.4GHz at the same time, which causes various signal interference problems. In this paper, we propose a novel channel reservation scheme, called IACR, to improve the energy efficiency of wireless sensor networks in an environment where interference occurs between various wireless technologies. The proposed scheme inserts a PN code into a long preamble for exchanging transmission status information between a transmitting node and a receiving node, thereby improving the transmission success probability while receiving less influence on transmission of other radio technologies. We performed an event-driven simulation and an experiment to measure the signal detection rate. As a result, it can be seen that the proposed technique reduces the packet drop rate by 15% and increases the discoverable distance of the control packet for channel reservation.
Game theory for resource sharing in large distributed systems
Authors: Riahi Sara , Riahi Azzeddine
Published: April 2019

In game theory, cooperative and non-cooperative approaches are distinguished in terms of two elements. The first refers to the player's ability to engage: in a non-cooperative context, they are entirely free to make decisions when they make their choices; However, in a cooperative context, they have the opportunity to engage contractually the strategies that should be adopted during the game, that during a phase of discussions held before the game and during combinations which may be formed.In this context, the problem is not so much to predict the outcome of the game between players to leave the benefit of cooperation. To achieve this, and this is the second major difference with the non-cooperative approach, it adopts an axiomatic approach (or normative) by which we set upstream properties a priori reasonable (or desirable) on the outcome of the game. The purpose of this paper is to present briefly the main types of non-cooperative games and the tools that allow them to be analyzed in a complete information context where all aspects of the game are well known to decision makers.
A new design of a microstrip rectenna at 5.8 GHz for wireless power transmission applications
Authors: Abdellah Taybi , Tajmouati Abdelali , Zbitou Jamal , Errkik Ahmed , Latrach Mohamed
Published: April 2019

Due to the ever-increasing power demand, the need of electricity and eco-friendly power in every nook and corner of the world, many reaserch topics have been devoted to deal with this problematic. This paper is taking part of the proposed solutions with the presentation of a novel 5.8 GHz rectenna system for wireless power transmission applications. In one hand, a miniaturized 5.8 GHz circular polarized patch antenna has been designed and simulated by using the Advanced Design System (ADS). In the other hand, a rectifier structure has been investigated and optimized by the use of the Harmonic Balance method available in ADS. The circuit is based on 5 HSMS2820 Schottky diodes implemented in a voltage multiplier topology and a load resistance of 1 KOhm. Both of the structures have been validated by simulation and experimental results and good agreement has been concluded.
Numerical simulation of electromagnetic radiation using high-order discontinuous galerkin time domain method
Authors: Pranowo Pranowo , Setyohadi Djoko Budiyanto
Published: April 2019

In this paper, we propose the simulation of 2-dimensional electromagnetic wave radiation using high-order discontinuous Galerkin time domain method to solve Maxwell's equations. The domains are discretized into unstructured straight-sided triangle elements that allow enhanced flexibility when dealing with complex geometries. The electric and magnetic fields are expanded into a high-order polynomial spectral approximation over each triangle element. The field conservation between the elements is enforced using central difference flux calculation at element interfaces. Perfectly matched layer (PML) boundary condition is used to absorb the waves that leave the domain. The comparison of numerical calculations is performed by the graphical displays and numerical data of radiation phenomenon and presented particularly with the results of the FDTD method. Finally, our simulations show that the proposed method can handle simulation of electromagnetic radiation with complex geometries easily.
Travel route scheduling based on user’s preferences using simulated annealing
Authors: Baizal Z. K. A. , Lhaksmana Kemas M. , Rahmawati Aniq A. , Kirom Mizanul , Mubarok Zidni
Published: April 2019

Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several user’s preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method.
Managing usability evaluation practices in agile development environments
Authors: Deraman Aziz Bin , Salman Fouad Abdulameer
Published: April 2019

Usability evaluation is a core usability activity that minimizes risks and improves product quality. The returns from usability evaluation are undeniable. Neglecting such evaluation at the development stage negatively affects software usability. In this paper, the authors develop a software management tool used to incorporate usability evaluation activities into the agile environment. Using this tool, agile development teams can manage a continuous evaluation process, tightly coupled with the development process, allowing them to develop high quality software products with adequate level of usability. The tool was evaluated through verification, followed by the validation on satisfaction. The evaluation results show that the tool increased software development practitioner satisfaction and is practical for supporting usability work in software projects. 
Earthquake trend prediction using long short-term memory RNN
Authors: Bhandarkar Tanvi , K Vardaan , Satish Nikhil , Sridhar S. , Sivakumar R. , Ghosh Snehasish
Published: April 2019

The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.
Feature selection, optimization and clustering strategies of text documents
Authors: Nikhath A. Kousar , Subrahmanyam K.
Published: April 2019

Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
An ensemble multi-model technique for predicting chronic kidney disease
Authors: N Komal Kumar , Tulasi R. Lakshmi , D Vigneswari
Published: April 2019

Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.
Hybrid method for automating generation of reticulated structures (lattice structures) in complex conception domains
Authors: Biallaten Zineb , Chiheb Raddouane , El Afia Abdellatif
Published: April 2019

A reticulated structure is an interconnexion of bars used to create industrial products. They are rigid and lighter than traditional structures. So they can be the best choice when material gain is an optimization purpose. Generating a reticulated structure automatically is a feature helping industrial players in the design phase. This generation depends on the kind of the conception domain. In this paper we propose a solution that generates a reticulated structure in an arbitrary domain with zero or several holes. The proposed solution is a hybrid method using a technique generating a reticulated structure in a convex conception domain and suggesting a criterion to validate generated segments. Our new algorithm uses a method of computational geometry. We also present a study of the behaviour of a reticulated structure generated using our tool by calculating the deformation energy of this structure.
Family relation and STR-DNA matching using fuzzy inference
Authors: Anggreainy Maria Susan , Widyanto M. Rahmat , Widjaja Belawati , Soedarsono Nurtami , Widodo Putut Tjahjo
Published: April 2019

Deoxyribose Nucleic Acid (DNA) are the basic elements that make up a whole section of an individual. The basic elements store information that is unique to each individual and will be passed down the generations. DNA also helps in identifying the father in paternity testing, locating missing person investigations, identifying victims in mass disasters. Identiﬁcation of the victims has a problem if the comparison the father and mother no instance the victim’s parents have died or are very far away from where the victim. Therefore, it is necessary to try to identify Short Tandem Repeat (STR) Inference of live family such as sibling, grandfather/grandmother, uncle/aunt, cousin and nephew. In this paper, we performed a method to measure the similarity of human DNA proﬁles using fuzzy similarity. In this fuzzy system,  DNA profile data is used as an input that stores human identity along with its DNA profile. The data entered is the result of polymerase chain reaction (PCR) identification which is an electropherogram consisting of 16 loci with two alleles for each locus.  Output in this fuzzy system is the value of individual similarity with reference and with similarity levels, namely small, medium and high.
Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution
Authors: Tahyudin Imam , Nambo Hidetaka
Published: April 2019

Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory.
Classification of medical datasets using back propagation neural network powered by genetic-based features elector
Authors: Lafta Hussein Attya , Hasan Zainab Falah , Ayoob Noor Kadhim
Published: April 2019

The classification is a one of the most indispensable domains in   the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Securing multi-tenancy systems through multi DB instances and multiple databases on different physical servers
Authors: Sastry J. K. R , Basu M. Trinath
Published: April 2019

Use of the same application by multiple users through internet as a service is supported by cloud computing system. Both the user and attacker stay in the same machine as both of them are users of the same application creating an in-secure environment. Service must ensure secrecy both at the application and data layer level. Data isolation and Application isolation are two basic aspects that must be ensured to cater for security as desired by the clients that accesses the service. In this paper a more secured mechanism has been presented that help ensuring data isolation and security when Multi-tenancy of the users to the same service has been implemented.
ImageSubXSS: an image substitute technique to prevent Cross-Site Scripting attacks
Authors: Nagarjun PMD , Ahamad Shaik Shakeel
Published: April 2019

Cross-Site Scripting (XSS) is one of serious web application attack. Web applications are involved in every activity of human life. JavaScript plays a major role in these web applications. In XSS attacks hacker inject malicious JavaScript into a trusted web application, execution of that malicious script may steal sensitive information from the user. Previous solutions to prevent XSS attacks require a lot of effort to integrate into existing web applications, some solutions works at client-side and some solutions works based on filter list which needs to be updated regularly. In this paper, we propose an Image Substitute technique (ImageSubXSS) to prevent Cross-Site Scripting attacks which works at the server-side. The proposed solution is implemented and evaluated on a number of XSS attacks. With a single line, developers can integrate ImageSubXSS into their applications and the proposed solution is able to prevent XSS attacks effectively.
Pseudo-Random Bit Generator Using Chaotic Seed for Cryptographic Algorithm in Data Protection of Electric Power Consumption
Authors: Elizalde-Canales Francisca Angélica , Rivas-Cambero Iván De Jesus , Rebolledo-Herrera Lucio Fidel , Camacho-Bello Cesar Joel
Published: April 2019

Cryptographic algorithms have played an important role in information security for protecting privacy. The literature provides evidence that many types of chaotic cryptosystems have been proposed. These chaotic systems encode information to obviate its orbital instability and ergodicity. In this work, a pseudo-random cryptographic generator algorithm with a symmetric key, based on chaotic functions, is proposed. Moreover, the algorithm exploits dynamic simplicity and synchronization to generate encryption sub-keys using unpredictable seeds, extracted from a chaotic zone, in order to increase their level of randomness. Also, it is applied to a simulated electrical energy consumption signal and implemented on a prototype, using low hardware resources, to measure physical variables; hence, the unpredictability degree was statistically analyzed using the resulting cryptogram. It is shown that the pseudo-random sequences produced by the cryptographic key generator have acceptable properties with respect to randomness, which are validated in this paper using National Institute of Standards and Technology (NIST) statistical tests. To complement the evaluation of the encrypted data, the Lena image is coded and its metrics are compared with those reported in the literature, yielding some useful results.
A novel framework for efficient identification of brain cancer region from brain MRI
Authors: Angadi Parvathi , Nagendra M , M Hanumanthappa
Published: April 2019

Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
Single view vs. multiple views scatterplots
Authors: Manuri Federico , Sanna Andrea , Lamberti Fabrizio
Published: April 2019

Among all the available visualization tools, the scatterplot has been deeply analyzed through the years and many researchers investigated how to improve this tool to face new challenges. The scatterplot visualization diagram is considered one of the most functional among the variety of data visual representations, due to its relative simplicity compared to other multivariable visualization techniques. Even so, one of the most significant and unsolved challenge in data visualization consists in effectively displaying datasets with many attributes or dimensions, such as multidimensional or multivariate ones. The focus of this research is to compare the single view and the multiple views visualization paradigms for displaying multivariable dataset using scatterplots. A multivariable scatterplot has been developed as a web application to provide the single view tool, whereas for the multiple views visualization, the ScatterDice web app has been slightly modified and adopted as a traditional, yet interactive, scatterplot matrix. Finally, a taxonomy of tasks for visualization tools has been chosen to define the use case and the tests to compare the two paradigms.