bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Analytics on Restaurants
Authors: Sharma Amratansh , E. Poovammal , Joshi Jugal
Published: May 2018

In the phase of today’s world when data is boosting in myriad amount and the information is on its peak due to digital revolution, it becomes very crucial to handle and manage the data and convert it into useful information so that it can be analyzed well and be productive enough. Whether, its health informatics, business analytics or any domain, data driven decisions are need to be taken which should be accurate and efficient enough to have fruitful impact for humanity and the society. Specifically, in this project the main aim is specifically narrowed down to perform analytics in the restaurant.  By observing various trends of data in different categorizations, we will be applying the predictive analytics, data mining techniques and algorithms to analyze the data and come out with innovative suggestions and predictions, which would help to ameliorate the profits of hotels and restaurants business and will serve the demands of customers.
Energy efficient enhanced tree structured compression model (ET-CM) for data aggregation in wireless sensor networks
Authors: Srikanth Nandoori , Siva ganga prasad M
Published: May 2018

Compression, is a typical strategy to decrease information measure by taking care of information excess, can be utilized as a part of postpone delicate remote sensor systems (WSNs) to diminish end-to-end bundle delay as it can lessen parcel transmission time and conflict on the remote channel. All together for remote sensor systems to misuse flag, flag information must be gathered at a large number of sensors and must be shared among the sensors. Huge sharing of information among the sensors repudiates the prerequisites (vitality effectiveness, low inactivity and high exactness) of remote organized sensor. This paper manages the investigation of compressive proportion and vitality utilization in the system by contrasting and the current compressive strategies.
Power System Stability Analysis of SMIB with FOPID using Cuckoo Search Optimized Differential Evaluation
Authors: M. Gajare Atul , R. P. Singh Dr , M. U. Nemade Dr
Published: May 2018

Due to complexity in the power system there is always a loss of the stability due to the fault. Whenever a fault is intercepted in system, the whole system goes to severe transients. These transients cause oscillation in phase angle which leads poor power quality. The nature of oscillation is increasing instead being sustained, which leads system failure in form of generator damage. To reduce and eliminate the unstable oscillations one needs to use a stabilizer which can generate a perfect compensatory signal in order to minimize the harmonics generated due to instability.This paper presents a Power System stabilizer to reduce oscillations due to small signal disturbance. Additionally, a hybrid approach is proposed using FOPID stabilizer with the PSS connected SMIB. Cuckoo Search (CS) Optimized Differential Evaluation (DE) approach is used for the parameter tuning of the stabilizer. The efficiency of proposed approach is observed by rotor angle and power angle deviations in the SMIB system.
Topology Optimization of Dual Octagonal Array antenna for low frequency applications
Authors: Venu Madhav P , M SivaGanga Prasad Dr
Published: May 2018

Objectives: Efficient antenna design for use in communication systems is altering the face of the antenna modeling. The ever-increasing demand for portable and efficient antennas is making researchers to develop innovative models [1] using advanced antenna modeling tools that comply with industrial needs and standards.  Methods/Statistical Analysis: Antennas with lower operating frequencies have the major constraints on its size, efficiency and gain. Study on matching techniques, feeding techniques was also to be considered.Findings: Micro strip patch antennas offers considerable amount of radiation, low cost when fabricated on FR4, light weight and are conformable to suite any application. This paper projects the design, simulation and testing of a dual octagonal patch, the topology algorithm [3] is used to optimize the size and shape of the patch where octagons are spaced in the form of an array to address optimization on size and fits into wireless applications. Application/Improvements: The proposed model is tested in the standard antenna test bench comprising of microwave integrated circuit analyzer receiver MIC10kit and found to operate at a resonant frequency of 1.8 GHz with good radiation characteristics.
Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network
Authors: D. Patil Dinesh , P. Singh R , M. Thakare Vilas , K. Gulve Avinash
Published: May 2018

This paper tried to address several topics concerning the analysis, synthesis and compression of the electrocardiogram signal (ECG) using the MIT database. We detect the R-wave by identifying the location of each interval delineating a QRS complex using unbiased and biased estimators. In the second part of the work, we segmented the signal into RR periods constituting the vectors of a data matrix, where we extracted its main components in order to reduce the size of the cardiac information, and then further reduced in addition the size by the use of a threshold on the signal. Then the classification is done for automatic detection of heart disease using Support Vector Machine (SVM) and Cuckoo Search Optimized Neural Network. ECG beats with 4 types of abnormalities (RBBB, APC, PVC and LBBB) from ECG records is retrieved from the MIT-BIH arrhythmia database. Analysis of the different groups shows the overall recognition performance was 99.50%. The worst is 99.63% for the RBBB class.
Spectrum Sensing in Cognitive Radio by Use of Volume-Based Method
Authors: S. Preetham C , Mahesh Ch , Saranga Haripriya Ch , Anirudh Ramaraju , S. Sireesha M
Published: May 2018

Spectrum sensing is the mission of finding the licensed user signal situation, i.e. to determine the existence and deficiency of primary (licensed) user signal, the recent publications random matrix theory algorithms performs better-quality in spectrum sensing. The RMT fundamental nature is to make use of the distributed extremal eigenvalues of the arrived signal sample covariance matrix (SMC), specifically, Tracy-Widom (TW) distribution which is useful to certain extent in spectrum sensing but demanding for numerical evaluations because there is absence of closed-form expression in it. The sample covariance matrix determinant is designed for two novel volume-based detectors or signal existence and deficiency cases are differentiated by using volume. Under the Gaussian noise postulation one of the detectors theoretical decision thresholds is perfectly calculated by using Random matrix theory. The volume-based detectors efficiency is shown in simulation results.
Analysis of channel allocation and management using tlbo, ga & de algorithms in cellular networks
Authors: Mounika M , Sharmila Md , Prasanna Gowri M , Tejasvi K , Siva Ganga Prasad M
Published: May 2018

Accuracy of spectrum sensing in cellular mobile networks can be increased by reducing the sensing error probability, call blocking probability & by increasing network throughput. In our Project work, an advanced algorithms like Teaching Learning Based Optimization (TLBO), Genetic (GA) and Differential Evaluation (DE) Algorithms are used to analyze the sensing error probability, call blocking probability and network throughput. We will propose a channel allocation and management scheme with the above mentioned algorithms for supporting mobile users. With the help of these algorithms, sensing error and call blocking probabilities are analyzed to find out an optimal value for reducing the errors to some extent and improving network throughput. Channel Allocation in cellular networks is a key aspect with channel impairments and non-ideal antenna patterns. Finally, performance analysis is done through comparison of simulation results.
Analysis of Driver Drowsiness Detection using EEG and EOG
Authors: K Firke Omprakash , Manish Jain Dr
Published: May 2018

This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.
Teaching Learning Based Algorithm for calculating optimal values of Sensing error probability, Throughput and Blocking Probability in Cognitive Radio
Authors: Katta Srinivas , Siva Ganga Prasad M , T.P Madhav B
Published: May 2018

We consider Teaching Learning Based Optimization (TLBO) to optimize the sensing error probability, Throughput and Blocking Probability in Cognitive Radio (CR) network. Sensing error probability, Throughput can be optimized by finding the optimal values of false alarm probability and detection probability. Blocking Probability can be optimized by finding the optimum value of number of guard channels. Results of TLBO are compared with Genetic Algorithm (GA) and Differential Evolution (DE) algorithm.
A Novel Approach for the Detection, Classification and Localization of Transmission Lines Faults using Wavelet Transform and Support Vector Machines Classifier
Authors: S.Wani Nilish , R. P. Singh Dr
Published: May 2018

Over the time, techniques have been developed whose approach has been linked mainly to the diagnosis of faults, which is why over the years these techniques have been improved little by little in order to complement or at best cases innovate the traditional methods used for the detection of faults from the mathematical point of view relying mainly on sophisticated methods and some of them related to artificial intelligence. Taking into account the aforementioned, in this paper we propose the use one of the branches of artificial intelligence, specifically automatic learning through the tool known as Support Vector Machines (SVM) to find a method with which is feasible to identify and classify the type of fault. For the creation of the mathematical model it is essential to have a database. The database consists of input data and output data, the input data are the detail coefficients obtained from the decomposition of the current and voltage signals using the Discrete Wavelet Transform (DWT). Meanwhile, the output data are the labels assigned and with which the model can identify and classify the different types of faults. Both current signals and voltage signals are generated based on an extensive simulation of faults along the longest transmission line that has a test system.
Analysis of MRI Data of Brain for CAD System
Authors: D Bonde Girish , Manish Jain Dr
Published: May 2018

Magnetic resonance imaging (MRI) technologies are currently one of the most effective tools in the diagnosis of a wide variety of socially significant pathologies including cancer, arteriosclerosis, episodes. Ischemic and neurodegenerative diseases [1, 2, 3, 4].This paper gives detailed idea of pre-processing, and segmentation(FCM, soft and hard) of MRI brain tumor images. This paper also insights the machine learning(SOM, NN and SVM) approach for automatic classification(PTPSA, fBM) of brain tissues. Different performance evaluation parameter and similarity metrics are discuss to define the efficiency of computer-aided diagnostic (CAD) system.
Performance study of MATLAB modelled PV panel and conventional PV panel interfaced with LabVIEW
Authors: K Jaiganesh , N Karuppiah , S Ravivarman , Asif Md
Published: May 2018

The maximum electrical energy conversion efficiency of the Solar PV panel is up to 22% in normal conventional roof- top system under the temperature of 25˚C on Standard Test Condition (STC). In Indian climatic conditions, the atmospheric temperature is mostly above 35˚C to 45˚C, it incites 35˚C to 80˚C temperature on the PV panel. The black body of the PV panel absorbs more heat. This temperature affects the electrical efficiency of the panel significantly. This paper proposes the mathematical modelling of the solar PV panel for different solar irradiation and the temperature. The experimental evaluation is conducted in the latitude of 11.36 (N) and longitude 77.82 (E). The testing and monitoring was done with LabVIEW based National Instruments hardware such as NI cDAQ-9178, NI DAQ - 9227 and NI DAQ 9225. The comparative study between the simulated result and real time hardware results are discussed in this paper. The test result shows that the output of the proposed model mismatches with the experimental output of the solar PV panel due to the negative correlation between the efficiency and temperature for variable irradiation condition. It shows a power difference of 9.41W between the output of the proposed model and the experimental setup.
Efficient adaptive noise cancellation techniques in an IOT Enabled Telecardiology System
Authors: Sulthana Asiya , Zia Ur Rahman Md
Published: May 2018

An increasing number of elderly­­­­ and disabled people urge the need for a health care monitoring system which has the capabilities for analyzing patient health care data to avoid preventable deaths. Medical Telemetry is becoming a key tool in assisting patients living remotely where a “Real-time Remote Critical Health Care Monitoring System” (RRCHCMS) can be utilized for the same. The RRCHCMS is capable of receiving and transmitting data from a remote location to a location that has the capability to diagnose the data and affect decision making and further providing assistance to the patient. During the cardiac analysis, several artifacts solidly affect the ST segment, humiliate the signal quality, frequency resolution, and results in large amplitude signals in ECG that simulate PQRST waveform and cover up the miniature features that are useful for clinical monitoring and diagnosis. In this paper, several leaky based adaptive filter structures for cardiac signal improvement are discussed. The Circular Leaky Least Mean Square (CLLMS) algorithm being the steepest drop strategy for dropping the mean squared error gives a better result in comparison with the Least Mean Square (LMS) algorithm. To enlarge the filtering ability some variants of LMS, Normalized Least Mean Square (NLMS), CLLMS, Variable Step Size CLLMS (VSS-CLLMS) algorithms are used in both time domain (TD) and frequency domain (FD). At last, we applied this algorithm on cardiac signals occurred due to MIT-BIH database. The performance of CLLMS algorithm is better compared to LLMS counterparts in conditions of Signal to Noise Ratio Improvement (SNRI), Excess Mean Square Error (EMSE) and Misadjustment (MSD). When compared to all other algorithms VSS-CLLMS gives superior SNRI. These values are 13.5616dB and 13.7592dB for Baseline Wander (BW) and Muscle Artifact (MA) removal.
Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications
Authors: Girika Jyoshna , Zia Ur Rahman Md
Published: May 2018

Removal of noise components of speech signals in mobile applications  is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault.  The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWNLMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI).
Real time Implementation of Face Recognition System on Raspberry Pi
Authors: Raju K , Y.Srinivasa Rao Dr
Published: May 2018

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance.
Optimal Block Diagonalization precoding scheme for MU-MIMO Downlink channel Using Particle Swarm Optimization
Authors: Saxena Shivangini , R.P. Singh Dr
Published: May 2018

As wireless communication turns out to be more common, the interest for higher rates of data transfer and continuous availability is expanding. Future wireless systems are provisioned to be very heterogeneous and interconnected. Higher data rates and Quality of Service (Qos) are two major expectations from any wireless technology. Fading is the main phenomenon which restricts the realization of Qos demand and higher data rates in wireless technologies. Fading is caused by obstacles in signal path which degrades the received signal’s quality. To mitigate the impact of fading on communication system the application of precoding techniques can be used. In this regard, this paper presents optimization of Block-Diagonalization (BD) based linear precoding scheme for multi-user multiple-input multiple output (MU-MIMO) systems. Simulation environment consists of a MIMO downlink scenario where a single base station (BS) with  antennas transmits to K receivers each with  antenna. The application of Particle Swarm Optimization (PSO) is used to find the optimal number of received antennas so as to reduce system complexity while maintaining Bit Error Rate (BER) performance of the system. MATLAB based simulation scenario is presented and evaluated over Rayleigh fading environment. Simulation results validate that the performance of Block– Diagonalization scheme can be improved up to 5dB with the application of Particle Swarm Optimization technique.
Novel Sgmentation Technique for Synthetic Aperture Radar Target Tracking using Hybrid PSO Method
Authors: Malakonda Reddy B , Zia Ur Rahman Md
Published: May 2018

In remote sensing applications, it is evidently electromagnetic imaging has more advantages than optical imaging due to its horizon. In such a contest synthetic aperture radars (SAR) plays a vital role. In SAR image processing, segmentation is a key step in identifying and tracking targets, terrain features.  Hence, this paper, we present an Improved hybrid PSO method proposed based on multilevel threshold for enhancing the image for segmentation. Experimental results indicate, the proposed methods enhance the edge features effectively with compare to Otsu, Modified Otsu and Region Based Active contour methods.
Land cover classification using landsat-8 optical data and supervised classifiers
Authors: V. Ramana Rao K , P. Rajesh Kumar Prof
Published: May 2018

Land use and land cover information of an area has got importance in various aspects mainly because of various development activities that are taking place in every part of the world. Various satellite sensors are providing the required data collected by remote sensing techniques in the form of images using which the land use land cover information can be analyzed.  Constistency of Landsat satellite is illustrated with two time periods such as Operational Land Imager (OLI) of 2013 and consecutive 2014 procured by earth explorer with quantified changes for the same period in visakhapatnam of hudhud cyclone. Since this city is consisting of mainly urban, vegetation, few water bodies, some area of agriculture and barren,five classes have been chosen from the study area. The results indicate that due to the hudhud event some changes took place.  vegetation and built-up land have been increased by An increase of 19.1% (6.3 km2) and 11% (5.36 km2) has been observed in the case of vegetation and built up area  where as a decrease of 1.2% (4.06 km2), 6.1% (1.70 km2) and 1.2% (0.72 km2) has been observed in the case of  agriculture, barren land, and water body respectively. With the help of available satellite imagery belonging to the same area and of different time periods along with the  change detection techniques landscape dynamics have been analyzed. Using various classification algorithms along with the data available from the satellite sensor the land use and land cover classification information of the study area has been obtained. The maximum likelihood algorithm provided better results compared to other classification techniques and the accuracy achieved with this algorithm is 99.930% (overall accuracy) and 0.999 (Kappa coefficient).
Constrained synthesis of the Linear Antenna Array using Social Group Optimization
Authors: Rahiman Sheik Abdul , Kalva Sri Rama Krishna Dr
Published: May 2018

In this paper, novel algorithm known as social group optimization is use for array synthesis problem. The algorithm is implemented to the electromagnetic problem solving and its performance is evaluated. The array design is carried out with the objective of sidelobe level (SLL) suppression with uniform beam width (BW) constraint. The amplitude only technique is used to determine the coefficients of current excitation which produce the desired radiation pattern with the objectives and constraints. The analysis is carried out in terms of radiation pattern for different length of linear arrays. The simulation based experimentation is carried out in Matlab.
Optimize Transmission of Image over Integrated Mobile WiMAX and WiLAN Network
Authors: More Anant R. , Vishal A. Wankhede , Prasad M.S.G.
Published: May 2018

The problem of multiuser downlink resource allocation in Mobile WiMAX system was studied. Three different network scenarios considered for the transmission of image data. Resource allocation is done using Active Set Optimization and Genetic Algorithm, for all the three network scenarios. Simulation results show that information fed back and association among subcarriers play vital role to improve system performance.  As compared to reduced complexity, resource allocation using Active Set Optimization and Genetic Algorithm seems especially attractive as the number of users increases for Image Data. It is observed through simulation that the System using Genetic Algorithm performs better than Active Set.  Linear Resource Allocation in terms of significantly decreasing the computational complexity and achieve higher capacities, while being applicable to a more general class of systems. As Genetic Algorithm follows constraints of Stochastic processes it didn’t give results highest in all Experiment Conducted. It is also observed that for Integer-Binary Objective function Performance of Genetic Algorithm is not that much satisfied because of its stochastic processing. Also processing time required to reach final optimize variables values is too much.
Efficient Routing In Wsn Using Enhanced Fuzzy Logic
Authors: Sarath kumar A , Durga Kaveri M , B.V Bhargavi K , Naga Swetha N , Priyanka K
Published: May 2018

In order to gather info additional precisely, wireless detector networks (WSNs) square measure divided into clusters. The cluster provides a good merit to make longer the period of WSNs. Topical clump comes close to usually use 2 methods: choosing cluster heads with additional enduring energy, and turning cluster heads sporadically, to distribute the energy consumption among nodes in every cluster and extend the network period. However, most of the previous algorithms haven't thought of the expected residual energy, that is, that the predicated left behind energy for being hand-picked as a cluster head and running around. During this paper, a fuzzy-logic-based clump approach [22] with associate degree conservatory to the energy postulation has been planned to lengthen the period of WSNs by equally distributing the work. The simulation results show that the planned come close to is additional more economical than alternative distributed algorithms. It's believed that the practice given during this paper can be any applied to extensive wireless detector networks.
An efficient magnetic resonance brain image classifier using tetrolet transform and kernel support vector machine based on OTSU binarization
Authors: M. Vasim babu Dr , Aparna D , Subhasri B , Mounika E , Chowdary.K Hema
Published: May 2018

Classification of tumor from cancer causing to non-cancer causing, it plays a main role in diagnosing the disease effectively without any flaws. In this proposed paper, a novel methodology is presented in order  to classify a given Magnetic resonant brain image as normal or abnormal using OTSU binarization segmentation with help of tetrolate transform. By replacing wavelet transform with tetrolate transform the classification is made efficient in case of images with geometric shapes. A good number of features are extracted by using OTSU binarization from edge-based segmentation, the more number of features makes the classification for effective and accurate. The image is finely segmented pixel by pixel for good accuracy and about 12 features like We calculate four different type of accuracy like RBF (Radial Basis Function), linear, polygonal and quadratic based on image features. We performed our proposed methods with four different kernels LIN (Linear), HPOL (Homogeneous Polynomial), and IPOL and GRB (Gaussian Radial Basis function) kernel to achieve the highest classification accuracy. The work is added with advancement by using Graphical user interface (GUI), which makes the user comfortable in accessing the method where most of the users are from clinical background and are not aware of any software and their usag.
Efficient Adaptive Exon Prediction for DNA study using Proportionate LMS Variants
Authors: Putluri Srinivasareddy , Zia Ur Rahman Md
Published: May 2018

In the field of Bio-informatics, locating the exon fragments in a deoxyribonucleic acid (DNA) sequence is an important and vital work. Study of protein coding regions is a wide phenomenon in identification of diseases and design of drugs. The regions of DNA that have the protein coding information are termed as exons. Hence identifying the exon segments in a genomic sequence is a crucial job in bio-informatics. Three base periodicity (TBP) has been observed in the regions of DNA sequences can be easily determined by applying signal processing methods. Adaptive signal processing techniques found to be useful than other available methods. This is due to their unique capability to alter weight coefficients based on genomic sequence. We propose efficient adaptive exon predictors (AEPs) based on these considerations using Proportionate Normalized LMS (PNLMS) algorithm and Maximum Proportionate Normalized LMS (MPNLMS) algorithm to improve exon locating ability and better convergence. To ease the complexity of computations in the denominator during filtering process, proposed AEPs using PNLMS and its maximum variants are combined with signature algorithms. Hybrid variants of proposed AEPs include PNLMS, DCPNLMS, ECPNLMS, SSPNLMS, MPNLMS, MDCPNLMS, MECPNLMS and MSSPNLMS algorithms. It was shown that the AEP based on MDCPNLMS is superior in applications of exon identification depending on performance measures with Sensitivity 0.7346, Specificity 0.7483 and precision 0.7325 for a genomic sequence with accession AF009962 at a threshold of 0.8. Finally the capability of several AEPs in predicting exon locations is verified using different DNA sequences found in National Center for Biotechnology Information (NCBI) gene database.
Improvement of damping properties of structural member using soft material at support
Authors: Kodavanla Babitha , A. Barai Dr , B. S. Dadapeer A , Threza Mery , Hanuma P , Madhavi N
Published: May 2018

Coincidental plane vibration in the midst of flight isn't unusual. There are different purposes behind plane vibration, including landing gear increase and withdrawal, extension of speed brakes, free play in versatile surfaces, and breakdowns of frameworks. Air vehicles have an airframe structure which incorporates every single auxiliary part. This task manages Improvement of damping properties of the basic part utilizing a delicate material at the help. So in this task considered a cantilever auxiliary part made up of composite and aluminum materials and delicate material is elastic, settling for support. How the damping properties are changing in basic part with and without delicate material, and discover which structure is having higher damping properties. Consequently, the objective of the present work is to demonstrate that the damping will enhance when the delicate material utilized at the help and composite has higher damping than aluminum.
Critical evaluation of classifiers in data stream mining
Authors: Agrawal Lalit , Adane Dattatraya
Published: May 2018

Over past decade there has been a significant increase in the volume of online data. Extracting meaningful knowledge from this high volume data is considered as important aspect of research. It is very difficult to completely store full data, because of its perpetual nature. Therefore, analysis is needed while the “data is moving”. This moving data is known as data stream and analyzing it without storing it completely is termed as data stream mining. In recent years, many new techniques have been proposed to overcome the challenges of data stream mining. In this paper, we review the operation of popular streaming algorithms highlighting their strength and weaknesses. We also evaluate the classifiers used in these algorithms against two popular benchmark datasets namely (a) forest cover (forest) and (b) german credit available at UCI repository. Finally, we present our critical observation and draw conclusions on the basis of our analysis.
Solving non-convex economic dispatch with prohibited zones using artificial fish swarm optimization
Authors: Ko Jun Seog , Salkuti Surender Reddy , Jung Chan Mook
Published: May 2018

In this paper, a novel approach is proposed to solve the non-convex and discontinuous economic dispatch (ED) problem of power system with thermal power plants. All the practical constraints (loss constraint, generators ramp rate constraints and network constraints) are considered for solving the ED problem. Here, the proposed ED problem is solved by considering the generators with valve point loading (VPL) effects and prohibited operating zones (POZs) effects. In this paper, to solve this practical ED problem, an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA) is utilized. The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed ED approach, 3 test systems (3, 6 and 20 generating unit systems) are considered. The obtained results are compared with other algorithms reported in the literature.
Feature Extraction in JPEG domain along with SVM for Content Based Image Retrieval
Authors: Hussain D. Mansoor , Surendran D. , Begum A. Benazir
Published: May 2018

Content Based Image Retrieval (CBIR) applies computer vision methods for image retreival purposes from the databases. It is majorly based on the user query, which is in visual form rather than the traditional text form. CBIR is applied in different fields extending from surveillance to remote sensing, E-purchase, medical image processing, security systems to historical research and many others. JPEG, a very commonly used method of lossy compression is used to reduce the size of the image before being stored or transmitted. Almost every digital camera in the market are storing the captured images in jpeg format. The storage industry has seen many major transformations in the past decades while the retrieval technologies are still developing. Though there are some breakthroughs happened in text retrieval, the same is not true for the image and other multimedia retrieval. Specifically image retreival has witnessed many algorithms in the spatial or the raw domain but since majority of the images are stored in the JPEG format, it takes time to decode the compressed image before extracting features and retrieving. Hence, in this research work, we focus on extracting the features from the compressed domain itself and then utilize support vector machines (SVM) for improving the retrieval results. Our proof of concept shows us that the features extracted in compressed domain helps retrieve the images 43% faster than the same set of images in the spatial domain and the accuracy is improved to 93.4% through SVM based feedback mechanism.
Analytical and Experimental Study on Composite Deep beams with Rolled section reinforcement
Authors: Pavani Konuri , Harsha G. Sri
Published: May 2018

An experimental study was conducted on three RC Deep beams along with Analytical simulation under three point loading in which one specimen was provided with Rolled section reinforcement. The specimens are designed according to IS 456:2000 . In one specimen the total reinforcement area was replaced with rolled I-Section. Steel plate grade of MS250 of 3mm thickness was molded as rolled I- Section. Various parameters like flexural behavior, shear strength, deflection, crack width, stress and ductility were calculated. For improving the bond strength between steel section and concrete in composite member, shear struts are provided. The length to depth ratio (l/d) was 1.875 and shear span to depth ratio (a/d) was 0.75. Finite element simulation was carried out by using ANSYS 18.1. and comparison was made  in between Analytical and Experimental results.
Multicast Utility Maximization with Load Balanc-ing in Private Cloud Environment
Authors: Senthamarai N , Vijayalakshmi M
Published: May 2018

Multicast is a technique for one-to-many communication over the network. It plays important role in cloud computing and reduces the transmission overhead in the private cloud environment. In this paper, build an efficient multicast tree for the multicast routing problem in which a network consists of different categories of nodes, where each category can have one or more nodes of the same characteristic which is different from the characteristics of nodes from other categories. So it is used to reduce the message traffic in such a network, to build a multicast tree and minimize the queuing delay using multicast selection algorithm.
Efficient NN Spatial Keyword Search Using Spatial Inverted (SI) Index
Authors: Vishnupriya B.A. , Senthamarai N. , Bharathi S.
Published: May 2018

"Spatial information mining", or learning revelation in spatial database, alludes to the illustration out of concealed information, spatial relations, or different examples that are not unequivocally put away in spatial databases. To get to the spatial database alongside the catchphrase another kind of inquiry called spatial watchword question is utilized. A spatial watchword inquiry get client area and client given catchphrases as contentions and gives web protests that are spatially and literarily material to these information. The current answers for such inquiries depend on IR2-tree that has a couple of inadequacies as space utilization and event of false hit is extremely huge when the question of the last outcome is far from the inquiry point .To beat this issue a novel file structure called Spatial Inverted file is proposed. Presently a-days use of portable is expanding enormously .In the versatile system an intermediary is set between base station and Location Based Server (LBS).This intermediary utilizes the Spatial modified file procedure to answer the SK inquiry by utilizing spatial data from the base station and printed data from the client question. The outcome from the SI record is given to two file structure in the intermediary called EVR Tree and Grid list. The Estimated Valid Region (EVR) for the present area of the client and required spatial articles are produced and come back to the client. On the off chance that the EVR is absent in the two file structure of intermediary it offer question to LBS. In the event that the client given inquiry is miss written or miss spelled it can be oversee by SI record utilizing n gram/2L Approximation file.
Emotion Recognition Based on General-ized Gamma Distribution
Authors: .T Sujeeth , Srinivas Y. , Vadaparthi Nagesh
Published: May 2018

It is highly difficult to identify the emotions of a person. Literature accessible to recognize the emotions in case of immobilized personnel is limited to the outcome obtainable from the machines only. In this process, brain computer communication is utilized using neuro-scan machines like Encephalography (EEG), to recognize the feeling of immobilized persons. It uses the physiological signals accessible from EEG data extracted from the brain signals of immobilized personnel and tries to find out the emotions, but these results vary from machine to machine, and there exists no consistency by which one can identify the thoughts of the brain diseased personnel precisely. In this manuscript a novel technique is projected by using Generalized Gamma Mixture models (GGMM). The advantage of considering GGMM is its ability of extracting the emotions closely even in a noisy environment. The outcomes of the proposed method exceed the accuracy rates of conventional systems.
Hybrid Ant Colony Optimization-Genetics Algorithm to Minimize Makespan Flow Shop Scheduling
Authors: Panggabean Jonas Franky R.
Published: May 2018

Flow shop scheduling is a scheduling model in which the job to be processed entirely flows in the same product direction / path. In other words, jobs have routing work together. Scheduling problems often arise if there is n jobs to be processed on the machine m, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. In research of Zini, H and ElBernoussi, S. (2016) NEH Heuristic and Stochastic Greedy Heuristic (SG) algorithms. This paper presents modified harmony search (HS) for flow shop scheduling problems with the aim of minimizing the maximum completion time of all jobs (makespan). To validate the proposed algorithm this computational test was performed using a sample dataset of 60 from the Taillard Benchmark. The HS algorithm is compared with two constructive heuristics of the literature namely the NEH heuristic and stochastic greedy heuristic (SG). The experimental results were obtained on average for the dataset size of 20 x 5 to 50 x 10, that the ACO-GA algorithm has a smaller makespan than the other two algorithms, but for large-size datasets the ACO-GA algorithm has a greater makespan of both algorithms with difference of 1.4 units of time.
Registration System and UTM Games Decision Using the Website Application
Authors: Azura Ahmad Fauzy Fatin , Ali Shah Zuraini , Rohmat Saedudin Rd , Kasim Shahreen , Alyani Azadin Ayu , Saleh Ahmar Ansari , Hidayat Rahmat
Published: May 2018

UTM Games is an annual sports tournament organized by UTM Sports Excellence involving students, staff team, PELUAS (International Student Team) and International Campus Kuala Lumpur. There are 14 types of contested games such as volleyball, soccer, squash, netball, bowling, badminton, kayaking, futsal, sepak takraw, table tennis, tennis, basketball, cycling, and lawn bowls. Currently registration and review of result for UTM Games are still done manually and on papers for some of the residential colleges. While for some other residential colleges, registration and review of results are done by web applications such as in Tun Ghafar Baba College (KTGB). Therefore, the Registration and Review of Results for UTM Games using Smart Phone is developed because most users nowadays have a smartphone and internet access. In addition, the system allows the user to see the results of UTM Games without having to log into social sites such as Facebook. The system is developed using ASP.Net technology and Microsoft Visual Studio 2010. The methodology used in developing this system is a prototype evolving methodology. This methodology allows the user to provide feedback and insights to add dynamically. The prototype will be modified according to the views and feedback given. The system is a platform to make it easier for managers to register for the game. In addition, the system is developed to enable students to review results from UTM Games more quickly and accurately.
Interpretation of the Meaning of Mosque Architecture : A Case Study Mosque 99 Cahaya in Lampung, Sumatera Island, Indonesia
Authors: Salura Purnama , Clarissa Stephanie
Published: May 2018

This research traced the relationship that exists between the expressions of mosque architecture with the meaning created. The method used for this research is: Firstly, recording building which is based on the theory about the scope of the building. Secondly, described the entire composition and building properties. Thirdly, interpreted the meaning that is created both on the outside and in the view of the space inside. Based on the results of the analysis, it can be concluded that the outside architectural form of the mosque tends to be interpreted as an abstract monument. While it’s inner space interpreted as a shelter that has nothing to do with its function as a religious building. This research is expected to give theoretical contribution to the knowledge of architecture that is by formulating new method to interpret expression of architectural form in general and specifically on the mosque architecture. While in practice, this research could be used as a reference in designing mosque architecture. In addition to scientific and architectural practices, this research could be used as a source of knowledge and offered an alternative perspective for the wider community in appreciating the work of architecture
Implementation of master-slave method on multiprocessor-based embedded system: case study on mobile robot
Authors: Wajiansyah Agusma , Purwadi Hari , Astagani Asrina , Supriadi Supriadi
Published: May 2018

In this research the master-slave method implemented on an embedded system using 3 processor applied to the mobile robot, to know the speed of program execution of robot. As a comparison is also used a robot with an embedded system based on single processor. From the experimental results, by applying the slave master method obtained the execution time of 546,5 μs and the number of iteration 1079, while for single processor-based system obtained execution time average 67828 μs and the amount of iteration average 147 times. Where the number of iterations is obtained by running the robot for 10 s. From this experiment, it can be concluded that there is a performance increase of 7.3% when compared to embedded systems based on single processor.
Neural network based projection of electricity demand in Indonesia using repetitive training method
Authors: . Arbain , Zainuddin Mohammad , Umma Aziza Gaffar Emmilya , Rizal Ansar
Published: May 2018

Indonesia Energy Outlook (IEO) 2016 published by BPPT projected the electricity demand in 2025 significantly will increase more than twice to 513 TWh from 203 TWh in 2015. This projection is based on the target of 100% electrification ratio in 2025. Assuming an average population growth of 1.2% in 2025 and a nominal GDP growth of 5.02% in 2014 which are expected to increase to 8% in 2025.This study projected the total electricity demand for the period 2016-2025 based on GDP, population, and electricity sales per sector (household, commercial, and industry) from the period of 2000-2015. Time series data modeling using Auto Regressive (AR) model and Autoregressive model with exogenous input (ARX) implemented using Artificial Neural Network Back-Propagation (ANN-BP). The repetitive training method is used to achieve the specified target error.
Image encryption using vigenere cipher with bit circular shift
Authors: Susanto Arief , Khotimah Tutik , Taufik Sumadi Muhammad , Warsito Joko , . Rihartanto
Published: May 2018

Cryptography is a method of securing text data, images and sound in order to secure its confidentiality and to minimize data stealing, attack, etc. The purpose of this study is to improve classic Vigenere cipher using bit circular shift on image encryption. This experiment uses both RGB and grayscale images as samples and shows that Vigenere cipher with bit circular shift has better performance when encrypting an image both visually and its randomness. It is obtained the MAE values of images of final encryption process are 81.702637 of RGB and 73.678756 of grayscale, and the final decryption process of 0 for both images. The randomness of images of the final encryption process is shown by its correlation coefficient values; it is 0.033857 of RGB and -0.058501 of grayscale.
Prediction of electricity sales using neural based inverse distance weighting method
Authors: . Bustani , Pradana Sunu , . Mulyanto , . Nurjanana
Published: May 2018

Prediction of electricity sales becomes important for State Electricity Company of Indonesia (PLN) in order to estimate the Statement of Profit and Loss in next year. To obtain good predictive results may require many variables and data availability. There are many available methods that do not require so many variables to get predicted results with a good approximation. The aim of this study was to predict electricity sales by using an interpolation method called IDW (Inverse Distance Weighting). Several data samples are mapped into Cartesian coordinates. The data samples used are power connected to the household (X-axis), to industry (Y-axis), and electricity sales (Z value). Firstly, the sampled data clustered by using SOM algorithm. The Z value in each cluster is predicted by using the IDW method. The prediction results of IDW method are then optimized using ANN-BP (Artificial Neural Network Back Propagation). The trained net structure is then used to predict the electricity sale in next year.