bluuebunny/crossref_metadata_2025 huggingface dataset:
A study of micro structural, magnetic and electrical properties of La-Co-Sm nanoferrites (LCSF) synthesized by sol-gel method
Authors: Wadi Kadhum Jassem , Abbas Hayder Abdulameer , Ali Adnan Hussein , Hasan Ban Mohammed
Published: March 2020

A Lanthanum (La3+) doped Samarium-Cobalt nanoferrites (La_x,Co_0.2,Sm_0.2,Fe_(2-x) O_4, where x=0.0,0.5,1.0) have been synthesized by sol-gel method in citrate media. Obtained spinal ferrites micro structure properties have been investigated by XRD, FTIR, SEM-EDX, and TEM-SAED techniques. All the samples are nano in size with significant hysteresis. Micro structural analysis by XRD confirms the obtained samples showing the single phase cubic spinal structures with an average crystal size found from 12 nm to 25 nm, while the average particles sizes identified from TEM analysis are ranging from 21.5nm-26.8 nm (~23.4nm) and from 20.5 nm to 28(~26.4nm) nm for x=0.5,1.0. The lattice parameter found to be a= 8.402, 8.423, 8.467Å for the respective values of x= 0.0, 0.05, and 1.0. Electrical properties show increase in dc resistivity with increase in La3+ ion concentration. Finally, it was concluded that the doping of Lanthanum ion (La3+) in the ferrites structure is found to influencing the structural and electrical properties without scarifying the ferromagnetic character.
Enhancing the photocatalytic activity of commercial P25 TiO2 powder by combining with handmade ni-doped TiO2 powder
Authors: Kurokawa Yoshiki , Nguyen Dang Trang , Taguchi KozoK
Published: March 2020

Titanium dioxide (TiO_2) is the most popular photocatalytic material. However, its operation is limited to UV light only. In this paper, we tried to improve the visible light responsiveness of TiO_2 by doping Nickel (Ni) using sol-gel method. By combining Ni-doped TiO_2 powder with commercially available P25 TiO_2 powder to make photocatalytic thin films, significant improvement in photocatalytic activity has been obtained. Furthermore, we also studied the relationship between the surface condition of photocatalytic thin films and their photocatalytic activity. The surface condition was improved by multilayer electrophoresis deposition method. Based on experimental results, by combining 10 - 20 wt% Ni-doped TiO_2 with P25 TiO_2, we could significantly enhance the photocatalytic activity of P25 TiO_2.
An analytical model for the current voltage characteristics of GaN-capped AlGaN/GaN and AlInN/GaN HEMTs including thermal and self-heating effects
Authors: Bellakhdar A. , Telia A. , Coutaz J. L.
Published: March 2020

We present an analytical model for the I-V characteristics of AlGaN/GaN and AlInN/GaN high electron mobility transistors (HEMT). Our study focuses on the influence of a GaN capping layer, and of thermal and self-heating effects. Spontaneous and piezoelectric polarizations at Al (Ga,In)N/GaN and GaN/Al(Ga,In)N interfaces have been incorporated in the analysis. Our model permits to fit several published data. Our results indicate that the GaN cap layer reduces the sheet density of the two-dimensional electron gas (2DEG), leading to a decrease of the drain current, and that n+-doped GaN cap layer provides a higher sheet density than undoped one. In n+GaN/AlInN/GaN HEMTs, the sheet carrier concentration is higher than in n+GaN/AlGaN/GaN HEMTs, due to the higher spontaneous polarization charge and conduction band discontinuity at the substrate/barrier layer interface.
Reinforcement learning based multi core scheduling (RLBMCS) for real time systems
Authors: Rinku Dhruva R. , AshaRani M.
Published: March 2020

Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system.
A hybrid image similarity measure based on a new combination of different similarity techniques
Authors: Hamza Nisreen Ryadh , Dihin Rasha Ail , Abdulameer Mohammed Hasan
Published: March 2020

Image similarity is the degree of how two images are similar or dissimilar. It computes the similarity degree between the intensity patterns in images. A new image similarity measure named (HFEMM) is proposed in this paper. The HFEMM is composed of two phases. Phase 1, a modified histogram similarity measure (HSSIM) is merged with feature similarity measure (FSIM) to get a new measure called (HFM). In phase 2, the resulted (HFM) is merged with error measure (EMM) in order to get a new similarity measure, which is named (HFEMM). Different kindes of noises for example Gaussian, Uniform, and salt & ppepper noiser are used with the proposed methods. One of the human face databases (AT&T) is used in the experiments and random images are used as well. For the evaluation, the similarity percentage under peakk signal to noise ratio (PSNR) is usedd. To show the effectiveness of the proposed measure, a comparision anong different similar technique such as SSIM, HFM, EMM and HFEMM are considered. The proposed HFEMM achieved higher similarity result when PSNR was low compared to the other methods.
Online signature verification using hybrid wavelet transform
Authors: Chavan Manoj , Singh Ravish R. , Bharadi Vinayak
Published: March 2020

Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vector is given to Left to Right HMM classifier to identify the genuine and forged signature. For HWT-1, DCT HAAR offers best FAR and FRR. . For HWT-2, KEKRE 128 offers best FAR and FRR. HWT-1 offers better performance than HWT- 2 in terms of FAR and FRR. As the number of states increase, the performance of the system improves. For HWT - 1, KEKRE 128 offers best performance at 275 symbols whereas for HWT - 2, best performance is at 475 symbols by KEKRE 128.
Optimization of deep learning features for age-invariant face recognition
Authors: Moustafa Amal A. , Elnakib Ahmed , F. Areed Nihal F.
Published: March 2020

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.
Comparative analysis and implementation of structured Edge Active Contour
Authors: Shah Brijesh N. , Bhalani Jaymin K.
Published: March 2020

This paper proposes modified chanvese model which can be implemented on image for segmentation. The structure of paper is based on Linear structure tensor (LST) as input to the variant model. Structure tensor is a matrix illustration of partial derivative information. In the proposed model, the original image is considered as information channel for computing structure tensor. Difference of Gaussian (DOG) is featuring improvement in which we can get less blurred image than original image.In this paper LST is modified by adding intensity information to enhance orientation information. Finally Active Contour Model (ACM) is used to segment the images. The proposed algorithm is tested on various images and also on some images which have intensity inhomogeneity and results are shown. Also, the results with other algorithms like chanvese, Bhattacharya, Gabor based chanvese and Novel structure tensor based model are compared.It is verified that accuracy of proposed model is the best. The biggest advantage of proposed model is clear edge enhancement.
Foreground algorithms for detection and extraction of an object in multimedia
Authors: V. Rekha , K. Natarajan , Rose J. Innila
Published: March 2020

Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.
High level speaker specific features modeling in automatic speaker recognition system
Authors: Singh Satyanand
Published: March 2020

Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
Elevation, pitch and travel axis stabilization of 3DOF helicopter with hybrid control system by GA-LQR based PID controller
Authors: Mohammed Ibrahim K. , Abdulla Abdulla I.
Published: March 2020

This research work presents an efficient hybrid control methodology through combining the traditional proportional-integral-derivative (PID) controller and linear quadratic regulator (LQR) optimal controlher. The proposed hybrid control approach is adopted to design three degree of freedom (3DOF) stabilizing system for helicopter. The gain parameters of the classic PID controller are determined using the elements of the LQR feedback gain matrix. The dynamic behaviour of the LQR based PID controller, is modeled and the formulated in state space form to enable utlizing state feedback controller technique. The performance of the proposed LQR based LQR controller is improved by using Genetic Algorithm optimization method which are adopted to obtain optimum values for LQR controller gain parameters. The LQR-PID hybrid controller is simulated using Matlab environment and its performance is evaluated based on rise time, settling time, overshoot and steady state error parameters to validate the proposed 3DOF helicopter balancing system. Based on GA tuning approach, the simulation results suggest that the hybrid LQR-PID controller can be effectively adopted to stabilize the 3DOF helicopter system.
Design topology of a remote-controlled bi-directional rotation of an A.C. fan with speed control
Authors: Anyaka Boniface O. , Ene Princewill C. , Fortune Amanze C. , Omeje Luke U. , Chijioke Kenneth C.
Published: March 2020

The use of remote control for alternating current fans in homes/offices is one of the applications of electronics that enhances human comfort. It should therefore not be seen as luxury, rather, it should be taken as a basic requirement of life. In this study, the bi-directional rotation and speed control of ac fan were achieved using ardiuno microcontroller and tuned capacitors circuit. The use of the new state-of- the art technique in this work is significant because it helped in over-coming the drawbacks associated with the use of inductive, resistive and phase angle controlled regulators. The significance of this study will be of great benefit to physically challenged and aged persons who need to have their comforts in their various places of sitting. The designed topology of the work was executed and achieved. Reliability test carried out on the finished work showed better efficiency.
Control of variable reluctance machine (8/6) by artificiel intelligence techniques
Authors: Chouitek Mama , Benouzza Noureddine , Bekouche Benaissa
Published: March 2020

The non-linearity of variable-Reluctance Machine (8/6) and the dependence of machine inductance on rotor position and applied current complicate the development of the control strategies of drives using variable-Reluctance Machine variable-Reluctance Machine (VRM). The classical-control algorithms for example of derived full proportional action may prove sufficient if the requirements on the accuracy and performance of systems are not too strict. In the opposite case and particularly when the controlled part is submitted to strong nonlinearity and to temporal variations, control techniques must be designed which ensure the robustness of the process with respect to the uncertainties on the parameters and their variations. These techniques include artificial-intelligence-based techniques constituted of neural networks and fuzzy logic. This technique has the ability to replace PID regulators by nonlinear ones using the human brain’s reasoning and functioning and is simulated by using MATLAB/Simulink software. Finally, by using obtained waveforms, these results will be compared.
Improved predictive current model control based on adaptive PR controller for standalone system based DG set
Authors: Ikaouassen Halima , Raddaoui Abderraouf , Rezkallah Miloud , Ibrahim Hussein
Published: March 2020

This paper investigates an improved current predictive model control (PCMC) strategy with a prediction horizon of one sampling time for voltage regulation in standalone system based on diesel engine driven fixed speed of a synchronous generator. An adaptive PR controller with anti-windup scheme is employed to achieve high performance regulation without saturation issues. In addition, new method to obtain the optimal parameters of the adaptive PR controller to achieve high performance during the transition and in steady state is provided. To balance the power at the point of common coupling (PCC) as well as to feed a clean power to the connected loads, a three-phase voltage source inverter (VSI) with LRC filter is controlled using the developed improved PCMC strategy, where the output filter current is controlled using the predicting of the system behaviour model in the future step, at each sampling prediction time. The performances of the proposed configuration and the improved control strategy are verified using Matlab/Simulink interface.
Rough set based QoS enabled multipath source routing in MANET
Authors: N. Prathviraj , Deshpande Santosh L
Published: March 2020

The single constrained Quality of Service (QoS) routing in Mobile Ad-hoc NETwork (MANET) is disastrous in consideration of MANET characteristics, inference, collision and link failure as it maintains a single path. The QoS enabled routing yields better packet delivery and maintains consistency among nodes in the network by incorporating multi-constrained and multipath routing. The Dynamic Source Routing (DSR) is best suited source routing algorithm to maintain multipath information at the source node, but performance degrades with larger number of mobile nodes. Multi-layer mechanism should be incorporated to maintain QoS metric information spreads across multiple layers of TCP/IP protocol stack. The proposed multipath QoS enabled source routing provides balanced routing by making use of all these features. The imprecise decision making strategy called Rough Set Theory (RST) is used at destination node for decision making. The Route REQuest (RREQ) messages coming from different routes are filtered by considering the QoS metrics of each and every route by making use of RST. The Route REPly (RREP) messages are generated and delivered to the source node for filtered RREQ messages. The proposed routing algorithm will reduce load on the network by reducing number of control messages exchanged for route establishment. This will evenly distribute load among all the nodes and it also avoid the scenarios like few nodes starved for resources. Finally, multipath routing always provides alternate routing option in case of route failure.
Performance evaluation of the IEEE 802.11n random topology WLAN with QoS application
Authors: Farej Ziyad Khalaf , Jasim Mustafa Mohammad
Published: March 2020

The IEEE 802.11n supports high data rate transmissions due its physical layer Multiple Input ‎Multiple Output (MIMO) advanced antenna system and MAC layer enhancement features (frame ‎aggregation and block acknowledgement). As a result this standard is very suitable for multimedia ‎services through its Enhanced Distributed Channel Access (EDCA). This paper focuses on ‎evaluating the Quality of Service (QoS) application on the performance of the IEEE 802.11n ‎random topology WLAN. Three different number of nodes (3, 9 and 18) random topology with one ‎access point are modeled and simulated by using the Riverbed OPNET 17.5 Modular to ‎investigate the Wireless Local Area Network (WLAN) performance for different spatial streams. ‎The result clarified the impact of QoS application and showed that its effect is best at the 18 node ‎number topology. For a 4x4 MIMO, when QoS is applied and with respect to the no QoS ‎application case, simulation results show a maximum improvement of 86.4%, 33.9%, 52.2% and ‎‎68.9% for throughput, delay, data drop and retransmission attempts, respectively. ‎
A combined spectrum sensing method based DCT for cognitive radio system
Authors: Falih Muntasser S. , Abdullah Hikmat N.
Published: March 2020

In this paper a new hybrid blind spectrum sensing method is proposed. The method is designed to enhance the detection performance of Conventional Energy Detector (CED) through combining it with a proposed sensing module based on Discrete Cosine Transform (DCT) coefficient’s relationship as operation mode at low Signal to Noise Ratio (SNR) values. In the proposed sensing module a certain factor called Average Ratio (AR) represent the ratio of energy in DCT coefficients is utilized to identify the presence of the Primary User (PU) signal. The simulation results show that the proposed method improves PU detection especially at low SNR values.
Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks
Authors: Vadivukkarasi K. , Kumar R.
Published: March 2020

Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.
Efficiency enhancement using optimized static scheduling technique in TSCH networks
Authors: Asuti Manjunath G. , Basarkod Prabhugoud I.
Published: March 2020

In recent times, the reliable and real-time data transmission becomes a mandatory requirement for various industries and organizations due to the large utilization of Internet of Things (IoT) devices. However, the IoT devices need high reliability, precise data exchange and low power utilization which cannot be achieved by the conventional Medium Access Control (MAC) protocols due to link failures and high interferences in the network. Therefore, the Time-Slotted Channel Hopping (TSCH) networks can be used for link scheduling under the IEEE 802.15.4e standard. In this paper, we propose an Optimized Static Scheduling Technique (OSST) for the link scheduling in IEEE 802.15.4e based TSCH networks. In OSST the link schedule is optimized by considering the packet latency information during transmission by checking the status of the transmitted packets as well as keeping track of the lost data packets from source to destination nodes. We evaluate the proposed OSST model using 6TiSCH Simulator and compare the different performance metrics with Simple distributed TSCH Scheduling.
A novel ensemble modeling for intrusion detection system
Authors: Priyadarsini Pullagura Indira , Anuradha G.
Published: March 2020

Vast increase in data through internet services has made computer systems more vulnerable and difficult to protect from malicious attacks. Intrusion detection systems (IDSs) must be more potent in monitoring intrusions. Therefore an effectual Intrusion Detection system architecture is built which employs a facile classification model and generates low false alarm rates and high accuracy. Noticeably, IDS endure enormous amounts of data traffic that contain redundant and irrelevant features, which affect the performance of the IDS negatively. Despite good feature selection approaches leads to a reduction of unrelated and redundant features and attain better classification accuracy in IDS. This paper proposes a novel ensemble model for IDS based on two algorithms Fuzzy Ensemble Feature selection (FEFS) and Fusion of Multiple Classifier (FMC). FEFS is a unification of five feature scores. These scores are obtained by using feature-class distance functions. Aggregation is done using fuzzy union operation. On the other hand, the FMC is the fusion of three classifiers. It works based on Ensemble decisive function. Experiments were made on KDD cup 99 data set have shown that our proposed system works superior to well-known methods such as Support Vector Machines (SVMs), K-Nearest Neighbor (KNN) and Artificial Neural Networks (ANNs). Our examinations ensured clearly the prominence of using ensemble methodology for modeling IDSs. And hence our system is robust and efficient.
Improving the data recovery for short length LT codes
Authors: Abdulkhaleq Nadhir Ibrahim , Abed Faeza Abbas , Hasan Ihsan Jabbar , Mahdi Falah Hasan
Published: March 2020

Luby Transform (LT) code is considered as an efficient erasure fountain code. The construction of the coded symbols is based on the formation of the degree distribution which played a significant role in ensuring a smooth decoding process. In this paper, we propose a new encoding scheme for LT code generation. This encoding presents a deterministic degree generation (DDG) with time hoping pattern which found to be suitable for the case of short data length where the well-known Robust Soliton Distribution (RSD) witnessed a severe performance degradation. It is shown via computer simulations that the proposed (DDG) has the lowest records for unrecovered data packets when compared to that using random degree distribution like RSD and non-uniform data selection (NUDS). The success interpreted in decreasing the overhead required for data recovery to the order of 25% for a data length of 32 packets.
Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)
Authors: Nihad El Ghouch , Mohamed Kouissi , El Mokhtar En-Naimi
Published: March 2020

Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article proposes a multi-agent adaptive learning system to make a real decision based on a current learning situation. This decision will be made by performing a hypride cycle of the Case-Based Reasonning approach in order to follow the learner and provide him with an individualized learning path according to Felder Silverman learning style model and his learning traces to predict his future learning status. To ensure this decision, we assign at each stage of the Incremental Hybrid Case-Based Reasoning at least one active agent performing a particular task and a broker agent that collaborates between the different agents in the system.
Enhanced exponential rule scheduling algorithm for real-time traffic in LTE network
Authors: A. Y. Yaqoob J. I. , Pang W. L. , Wong S. K. , Chan K. Y.
Published: March 2020

Nowadays, mobile communication is growing rapidly and become an everyday commodity. The vast deployment of real-time services in Long Term Evolution (LTE) network demands for the scheduling techniques that support the Quality of Service (QoS) requirements. LTE is designed and implemented to fulfill the users’ QoS. However, 3GPP does not define the specific scheduling technique for resource distribution which leads to vast research and development of the scheduling techniques. In this context, a review of the recent scheduling algorithm is reported in the literature. These schedulers in the literature cause high Packet Loss Rate (PLR), low fairness, and high delay. To cope with these disadvantages, we propose an enhanced EXPRULE (eEXPRULE) scheduler to improve the radio resource utilization in the LTE network. Extensive simulation works are carried out and the proposed scheduler provides a significant performance improvement for video application without sacrificing the VoIP performance. The eEXPRULE scheduler increases video throughput, spectrum efficiency, and fairness by 50%, 13%, and 11%, respectively, and reduces the video PLR by 11%.
SαS noise suppression for OFDM wireless communication in rayleight channel
Authors: Hamlili Heyem , Kameche Samir , Abdelmalek Abdelhafid
Published: March 2020

Orthogonal frequency division multiplexing (OFDM) is a form of multi-carrier transmission technique widely used in the modern wireless network to achieve high-speed data transmission with good spectral efficiency. However, in impulsive noise environement BER performances of these systems, originally designed for a Gaussian noise model, are much degraded. In this paper, a new symmetric-alpha-stable (SαS) noise suppression technique based conjointly on adaptive modulation, convolutional coding (AMC) and Recursive Least Square (RLS) filtering is presented. The proposed scheme is applied on OFDM system in Rayleigh fading channel. The transmissions are analyzed under different combinations of digital modulation schemes (BPSK, QPSK, 16-QAM, 64-QAM) and convolutional code rates (1/2, 2/3, 3/4). Simulation results show that our proposed hybrid technique provides effective impulsive noise cancelation in OFDM system and exhibits better BER performance.
An extensive research survey on data integrity and deduplication towards privacy in cloud storage
Authors: G. Anil Kumar , P. Shantala C.
Published: March 2020

Owing to the highly distributed nature of the cloud storage system, it is one of the challenging tasks to incorporate a higher degree of security towards the vulnerable data. Apart from various security concerns, data privacy is still one of the unsolved problems in this regards. The prime reason is that existing approaches of data privacy doesn't offer data integrity and secure data deduplication process at the same time, which is highly essential to ensure a higher degree of resistance against all form of dynamic threats over cloud and internet systems. Therefore, data integrity, as well as data deduplication is such associated phenomena which influence data privacy. Therefore, this manuscript discusses the explicit research contribution toward data integrity, data privacy, and data deduplication. The manuscript also contributes towards highlighting the potential open research issues followed by a discussion of the possible future direction of work towards addressing the existing problems.
Improving accuracy of Part-of-Speech (POS) tagging using hidden markov model and morphological analysis for Myanmar Language
Authors: Cing Dim Lam , Soe Khin Mar
Published: March 2020

In Natural Language Processing (NLP), Word segmentation and Part-of-Speech (POS) tagging are fundamental tasks. The POS information is also necessary in NLP’s preprocessing work applications such as machine translation (MT), information retrieval (IR), etc. Currently, there are many research efforts in word segmentation and POS tagging developed separately with different methods to get high performance and accuracy. For Myanmar Language, there are also separate word segmentors and POS taggers based on statistical approaches such as Neural Network (NN) and Hidden Markov Models (HMMs). But, as the Myanmar language's complex morphological structure, the OOV problem still exists. To keep away from error and improve segmentation by utilizing POS data, segmentation and labeling should be possible at the same time.The main goal of developing POS tagger for any Language is to improve accuracy of tagging and remove ambiguity in sentences due to language structure. This paper focuses on developing word segmentation and Part-of- Speech (POS) Tagger for Myanmar Language. This paper presented the comparison of separate word segmentation and POS tagging with joint word segmentation and POS tagging.
Impact of big data congestion in IT: an adaptive knowledge-based bayesian network
Authors: Farhana Soheli , Lajis Adidah , Long Zalizah Awang , Nasir Haidawati
Published: March 2020

Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Comparative study of user experience on mobile pedestrian navigation between digital map interface and location-based augmented reality
Authors: Brata Komang Candra , Liang Deron
Published: March 2020

Fast-paced mobile technology development has permitted augmented reality experiences to be delivered on mobile pedestrian navigation context. The fact that the more prevalent of this technology commonly will substituting the digital map visualization to present the geo-location information is still debatable. This paper comprises a report on a field study comparing about user experience when interacting with different modes of mobile electronic assistance in the context of pedestrian navigation interfaces which utilize location-based augmented reality (AR) and two-dimensional digital map to visualize the points of interest (POIs) location in the vicinity of the user. The study was conducted with two subsequent experiments in the Zhongli District, Taoyuan City, Taiwan. The study involved 10 participants aged between 22 and 28 years with different experiences in using smartphones and navigation systems. Navigation performance was measured based on a usability approach on pragmatic quality and hedonic quality like effectiveness (success rate of task completion), efficiency (task completion time) and satisfaction in real outdoor conditions. The evaluation findings have been cross-checked with the user’s personal comments. We aim at eliciting knowledge about user requirements related to mobile pedestrian interfaces and evaluating user experience from pragmatic and hedonic viewpoints. Results show that in the context of pedestrian navigation, digital map interfaces lead to significantly better navigation performance in pragmatic attributes in comparison to AR interfaces. Nevertheless, the study also reveals that location-based AR is more valued by participants in hedonic qualities and overall performance.
Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition
Authors: Nurhaida Ida , Ayumi Vina , Fitrianah Devi , Zen Remmy A. M. , Noprisson Handrie , Wei Hong
Published: March 2020

One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.
Finding the shortest path in a graph and its visualization using C# and WPF
Authors: Mavrevski Radoslav , Traykov Metodi , Trenchev Ivan
Published: March 2020

The shortest path problem is a classic problem in mathematics and computer science with applications in Economics (sequential decision making, analysis of social networks, etc.). The presented work is an example of realizing and applying of Dijkstra's algorithm to find the shortest path between two vertices in a connected, undirected graph, which is often a solved problem at a time annual International Olympiad in Informatics. For this purpose, are used the technologies, .NET 4.0, Visual Studio 2010, and WPF for the graphical user interface. The implemented program allows drawing an undirected graph, visualizing the shortest path between two vertices and finding its value. This software is a valuable tool for the study of Dijkstra's algorithm and is a great pedagogic instrument. All figures of path visualization included in this paper are actual screenshots of our visualization program.
Novel modelling of clustering for enhanced classification performance on gene expression data
Authors: V. Sudha , H. A. Girijamma
Published: March 2020

Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expression data. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.
Integrated approach for efficient power consumption and resource allocation in MIMO-OFDMA
Authors: B. Archana , Surekha T. P.
Published: March 2020

The growing interest towards wireless communication advancement with smart devices has provided the desired throughput of wireless communication mechanisms. But, attaining high-speed data packets amenities is the biggest issue in different multimedia applications. Recently, OFDM has come up with the useful features for wireless communication however it faces interference issues at carrier level (intercarrier interferences). To resolve these interference issues in OFDM, various existing mechanisms were utilized cyclic prefix, but it leads to redundancy in transmitted data. Also, the transmission of this redundant data can take some more power and bandwidth. All these limitations factors can be removed from a parallel cancellation mechanism. The integration of parallel cancellation and Convolution Viterbi encoding and decoding in MIMO-OFDMA will be an effective solution to have high data rate which also associations with the benefits of both the architectures of MIMO and OFDMA modulation approaches. This paper deals with this integrated mechanism for efficient resource allocation and power consumption. For performance analysis, MIMO-OFDMA system is analyzed with three different approaches likeMIMO-OFDM system without parallel cancellation (MIMO-OFDMA-WPC), MIMO-OFDMA System with parallel cancellation (MIMO-OFDMA-PC) and proposed IMO-OFDMA system with parallel cancellation and Convolution Viterbi encoding/decoding (pMIMO-OFDMA-PC &CVed) for 4x4 transmitter and receiver. Through performance analysis, it is found that the proposed system achieved better resource allocation (bandwidth) with high data rate by minimized BER rate and achieved least power consumption with least BER.
Advancement in infotainment system in automotive sector with vehicular cloud network and current state of art
Authors: S. Reshma , Chetanaprakash Chetanaprakash
Published: March 2020

The automotive industry has been incorporating various technological advancement on top-end versions of the vehicle order to improvise the degree of comfortability as well as enhancing the safer driving system. Infotainment system is one such pivotal system which not only makes the vehicle smart but also offers abundance of information as well as entertainment to the driver and passenger. The capability to offer extensive relay of service through infotainment system is highly dependent on vehicular adhoc network as well as back end support of cloud environment. However, it is know that such legacy system of vehicular adhoc network is also characterized by various problems associated with channel capacity, latency, heterogeneous network processing, and many more. Therefore, this paper offers a comprehensive insight to the research work being carried out towards leveraging the infotainment system in order to obtain the true picture of strength, limitation, and open end problems associated with infotainment system.
A secure image steganography based on JND model
Authors: Nguyen Tuan Duc , Le Dung Huu
Published: March 2020

Minimizing distortion produced by embedding process is very important to improve the security of hidden message and maintain the high visual quality of stego images. To achieve these objectives, an effective strategy is to perform pixel selection which is well-known as a channel selection rule. In this approach, a pixel associated with the smallest image degradation is chosen to carry secret bits. From these facts, in this paper, a new secure channel selection rule for digital images in spatial domain is designed and proposed. In this new approach, the modified matrix embedding method is utilized as data hiding method because it introduces more than one embedding change to be performed. This enables us to select a suitable pixel to embed message bits with less degradation yielded in a stego-image. In pixel selection of the proposed method, a just noticeable difference value and gradient value of a considering pixel are employed together. The experimental results (which were conducted on 10,000 uncompressed images) indicate that stego images of the proposed approach achieve a higher perceptual quality and security than those of the stego-images created by the previous approaches.
Available techniques in hadoop small file issue
Authors: Masadeh M. B. , Azmi M. S. , Ahmad S. S. S.
Published: March 2020

Hadoop is an optimal solution for big data processing and storing since being released in the late of 2006, hadoop data processing stands on master-slaves manner [1] that’s splits the large file job into several small files in order to process them separately, this technique was adopted instead of pushing one large file into a costly super machine to insights some useful information. Hadoop runs very good with large file of big data, but when it comes to big data in small files it could facing some problems in performance, processing slow down, data access delay, high latency and up to a completely cluster shutting down [2]. In this paper we will high light on one of hadoop’s limitations, that’s affects the data processing performance, one of these limits called “big data in small files” accrued when a massive number of small files pushed into a hadoop cluster which will rides the cluster to shut down totally. This paper also high light on some native and proposed solutions for big data in small files, how do they work to reduce the negative effects on hadoop cluster, and add extra performance on storing and accessing mechanism.
Improving the role of language model in statistical machine translation (Indonesian-Javanese)
Authors: Sujaini Herry
Published: March 2020

The statistical machine translation (SMT) is widely used by researchers and practitioners in recent years. SMT works with quality that is determined by several important factors, two of which are language and translation model. Research on improving the translation model has been done quite a lot, but the problem of optimizing the language model for use on machine translators has not received much attention. On translator machines, language models usually use trigram models as standard. In this paper, we conducted experiments with four strategies to analyze the role of the language model used in the Indonesian-Javanese translation machine and show improvement compared to the baseline system with the standard language model. The results of this research indicate that the use of 3-gram language models is highly recommended in SMT.
Gammu and kalkun for information services and sales based on information technology
Authors: Santoso Joseph Teguh , Wibowo Mars Caroline , Raharjo Budi , Mufadhol Mufadhol
Published: March 2020

The rapid development of information technology forces everything to be done sophisticated, easy, and practical. During the information technology revolution 4.0 which is commonly called the disruptive era, the people need communication to exchange information and data. One of the communication systems that has been found today is handphone technology that can be used to communicate remotely through short message services. It can be ascertained that almost every adult having a handphone can be even more than one number, so it is a top priority for market sharing in business activities by utilizing gammu and kalkun technology. Gammu which is an open source program application was developed to build SMS gateways and or while the kalkun program application is used for web based SMS management system. This paper describes the use of gammu and kalkun in information technology to develop and manufacture and manage SMS SERVER effectively and certainly can save costs. This method is more efficient because it allows companies to easily manage their own information dissemination and sales activities and according transactions to business needs, so that the budget spent by the company can be reduced and more economical.
A Literature review on industrially accepted MPPT techniques for solar PV system
Authors: Yadav Indresh , Maurya Sanjay Kumar , Gupta Gaurav Kumar
Published: March 2020

Solar energy is a clean renewable energy and it is available around 89,000 TW on the earth surface. To get maximum power from a solar PV system with minimum power transfer loss is one of the main design objectives of an energy transferring network. Power electronic devices perform a very important character for an efficient PV power tracking system control and either incorporates to transfer the generated power to the ac/dc grid or battery storage system. In this case the duty of the power electronics devices used in PV system is to track maximum power point under different operating conditions of environment, so that power tracking efficiency of solar PV system can be improved. This paper encapsulates based the on performance comparisions on the behavior of MPP under uniform and nonuniform operating conditions and selects the optimum duty cycle for industrially accepted MPPT techniques with their algorithm.