International Journal of Intelligent Engineering and Systems

1

Authors: Shilpi Singh, Raj Shree

Software regression testing is one of the most critical phases of software development life cycle, used by developers with the intent of detecting new faults to validate modified software prior to delivery to the customer. To validate updated features, new test cases are generated by the testers which increment the test suite size automatically. The resulting test suite may contain obsolete, redundant, and ambiguous test cases. Therefore, there is a strong requirement of an intelligent testing approach to reduce the test suite size by removing those unessential test cases economically. This paper proposed an interesting approach, which involves the combination of regression testing techniques: minimization, and prioritization both. The main focus is on multiple regression activities with multiple criteria rather than using only single activity to produce an optimal solution. In this paper clustering approach is also considered, which could simplify and enhance the minimization and prioritization task. To evaluate the effectiveness of the strategy, we performed an experimental investigation together with an eminent heuristic Harrold Gupta and Soffa (HGS), considering the testing measures of the minimized test suite size and fault coverage. The results show that, similarity-based greedy approach with multiple coverage criteria can be quite effective in terms of fault detection loss of reduced test suite without much affecting the percentage of suite size reduction.

21

Authors: Vedavathi Gauribidanur Rangappa, Sahani Venkata Appala Varaprasad Prasad, Alok Agarwal

The catastrophic heart diseases such as Myocardial Infarction (MI), Heart Failure (HF) and Ischemic Heart Disease (IHD) is a chain process leads to Coronary Artery Disease (CAD). The analysis of CAD from Electrocardiogram (ECG) signals by manual techniques are quite difficult. Therefore, there is need of techniques without human interaction for classifying the CAD should be improved. This work presented the recognition of five types of ECG beats by using a three-step system. In the first step, Pan-Tompkins algorithm (PTA) is used for detecting the peaks in ECG signals. The second step includes extraction of three interval features combined with ECG higher order statistics. In the third step for classifying ECG beats K-Nearest Neighbour (KNN) technique is employed. This approach analysed the heartbeats as normal or abnormal as accurately, and the experiments were conducted on MIT/BIH arrhythmia database for classifying the ECG signals. The results stated that the accuracy of the proposed approach is up to 98.40% for segregating the signals.

44

Authors: Ahmed Elhamraoui, Elhassane Abdelmounim, Jamal Zbitou, Hamid Bennis, Mohamed Latrach

A new dual-band monopole microstrip antenna with modified ground structure designed for Radio Frequency Identification (RFID) readers is proposed. The rectangular slots inserted in the ground plan permit to achieve dual frequencies and also provide a broadband operation at high frequency. The entire area of the proposed antenna is 33×35 mm2 and mounted on an FR4 substrate with dielectric permittivity constant 4.4, thickness of 1.6 mm and loss tangent of 0.025 and fed by a 50 Ohm microstrip line. This antenna has been analyzed and simulated to cover the RFID at 2.45/5.8 GHz. The simulation has been carried out by using two electromagnetic solvers ADS and CST Microwave Studio. By fabricating and measuring the performances of the prototype of the proposed optimal antenna, experimental results show that the dual operating bands excited with -10dB return-loss bandwidths of about 70 MHz centered at 2.45 GHz and of about 490 MHz centered at 5.8 GHz were obtained.

65

Authors: Fajar Astuti Hermawati, Handayani Tjandrasa, Nanik Suciati

The research proposed a method that combined non-deep learning detector that called Aggregated Channel Features (ACF) detector and Convolutional Neural Network (CNN) that named Faster R-CNN detector to extract a cross-sectional area of the fetal limb in an ultrasound image. This combination is appropriate to solve the problem of object detection where the object has no clear characteristic, it has shape variation, blurred, and no clear boundaries, which is difficult to solve using the common thresholding or the edge detection method. This method also deals with the ultrasound image analysis which the training set is small. The pre-trained CNN can establish the classification model from the small annotated training data. ACF detector provides the region proposals of the non-cross-sectional area as an input of pre-trained CNN. The proposed method could improve the average precision of detection result when it was compared with Faster R-CNN and ACF detector alone. Also, the combination method could reduce the elapsed time of the Faster R-CNN training phase significantly.

85

Authors: Saravanan Ramalingam, Pothula Sujatha

Stock Price Prediction (SPP) is a potential fiscally rewarding arena as it involves several dependent factors and complex structure. As the stock price fluctuates during every business day, it is nontrivial to forecast the stock prices by the buyers and sellers. Numerous methods have been developed for SPP based on Machine Learning (ML) techniques. In this work, an Ant Colony Optimization (ACO) based SPP model is introduced to forecast the stock prices accurately. The efficiency of the proposed ACO-SPP model is analyzed by comparing the performance with a set of traditional ML based classifiers such as Naive Bayes (NB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Radial Basis Function (RBF), J48, RF, Classification and Regression Tree (CART) and Olex-GA. To emphasize the benefits of ACO-SPP model, it is applied to a benchmarked dataset named Dow Jones Index (DJI) dataset and three datasets from Yahoo finance on daily, weekly and monthly basis. The experimental results infer that the ACO-SPP accurately predicts stock prices than the compared methods in terms of accuracy, F-score, AUC, Discriminant Power and Youden's Index.

108

Authors: Yadaiah Balagoni, Ramisetty Rajeswara Rao

The Apache Hadoop with cloud had become an emerging and popular service. Irrespective of its huge dominance in large scale data processing, it has challenges yet to be addressed. The primary challenges in yarn scheduler are the abilities to automate and control the resource allocation to different workloads in order to meet the deadline-based Service Level Agreement (SLA) in the cloud environment with optimal energy consumption. Our study with the Hadoop YARN addresses this problem in a controlled homogeneous environment. In cloud data-centers, heterogeneity had become a normal phenomenon. Hence, this paper proposes the problem of energy-aware heterogeneous Hadoop Yarn cloud with deadline based SLA. We proposed a SLA-Aware Green Scheduling (SAGS), a Dynamic Voltage/Frequency Scaling (DVFS) based approach along with SLA-Aware scheduling algorithm in the heterogeneous environment. We evaluated SAGS by using benchmark datasets and, compared its performance with previously proposed solutions. Our observation with experimental results shows that, the proposed approach outperforms existing approaches.

128

Authors: Hemalatha Kivudujogappa Lingappa, Hosahally Narayangowda Suresh, Sunil Kumar Manvi

In image segmentation field, the Fuzzy C-Means (FCM) algorithm is a well-known algorithm for its simplicity and membership function that can control the overlapped clusters effectively with a predefined number of clusters. Despite the fact, the standard FCM algorithm is noise sensitive. To solve the issue, we proposed a new method of clustering named Kernel Fuzzy C-means (KFCM) clustering. KFCM performed well in terms of clustering however, for pattern recognition KFCM has issues. The first one is grouping the similar objects in a single partition due to non-awareness of patterns and the second one is misclassification of data due to the standard structure of the membership subspace plane. Non-awareness of patterns of KFCM is solved by an Extreme Learning Machine (ELM) algorithm and Artificial Bee colony (ABC) algorithm utilized for optimizing the structure of the membership subspace plane. Experimental results showed that the proposed KFCM algorithm performed better segmentation for pattern recognition. At last effectiveness of the proposed algorithm has been evaluated based on comparing the K Means, FCM, spatial FCM, and KFCM algorithms in terms of centroids, segmentation accuracy, and pixel error. The proposed methodology improved the segmentation accuracy up to 0.8-5.5% compared to the existing methods.

148

Authors: Nam-Soo Kim

In this paper, we derive the effect of channel errors on the performance of user relaying cooperative NOMA system in closed-form. The max-min selection strategy is adapted to the selection of a relay among near users (NUs) and selection combining (SC) is applied to a far user (FU) for the performance improvements. As the previous studies found, the max-min relay selection without channel errors is effective to improve the outage performance. However, we demonstrate that the outage performance is highly sensitive to the channel error. It is also found that the outage probability of the relay user is more susceptible to the channel errors. As the channel correlation coefficient approaches to 0, the performance gains are banishing and the outage probability with the max-min relay selection becomes identical to that with the random relay selection. NOMA system has an optimal power allocation coefficient to minimize the outage performance, but as the correlation coefficient decrease, the effect of power allocation to the performance becomes lessen. The provided numerical results confirm that the derived expressions match well with Monte Carlo simulations.

167

Authors: Sivakumar Kaliappan

The Privacy Preserving Data Mining (PPDM) is known to be the most critical perspective among analysts. As privacy preserving data mining grants, sharing and exchanging of privacy susceptible data for analysis, it has exploited increasingly popular. Since one of the critical aspects of data mining is safeguarding privacy. The diverse technique is embraced for preserving privacy while maintaining the real characteristic of data under consideration. In the proposed work, the high-dimensional data are isolated into various parts by utilizing the k-mean clustering technique and each partition is considered as a cluster. By then the mean estimate of each cluster is processed, after that, the contrast between each cluster member and the mean of the cluster esteem is processed. In the succeeding stage, the clustered information is enhanced by utilizing the Ant Colony Optimization (ACO) algorithm. After that, the optimized clustered particles are perturbed by utilizing the Random Rotation Perturbation (RRP) algorithm which makes the values hard to be recognized. These perturbed values are then stored in the public cloud and the key parameters for randomizing and the clustering is stored in the private cloud. Our approach would contribute in the diminishment of a lot of storage in a private cloud, in case we essentially store the entire sensitive information on private clouds. The experimental results demonstrate that the RRP algorithm has better privacy preserving contrasted with the other existing technique.

190

Authors: Sumanth Venugopal, Guruprasad Nagraj

In day to day life, the process of projecting the correct information to the authorized person is more difficult, which makes complexity to the decision making process. Web Page Recommendation Systems (WPRS) used in various fields to identify the customer needs and to help the users to take appropriate decisions over the service or product according to his/her preference. The group of users with similar preference will be identified by using Possibilistic Fuzzy C-Means (PFCM) algorithm with an S3I Similarity Measure (SM). The proposed method will determine the gain and loss of the web users based on the web directories which can be modified by using Relevance Feedback Bayesian Network (RFBN) technique. The experimental results are conducted on the MNSBC dataset and the outcomes are compared with the existing methods like Singular Value Decomposition (SVD) methods. The method predicts the accuracy up to 85% when compared with the existing methods and the outcome results proved the effectiveness of the PFCM - RFBN method.

209

Authors: Mariyam Ouaissa, Abdallah Rhattoy

Machine to Machine (M2M) communication is a technology for machines communicating with each other without human intervention. Long Term Evolution (LTE) and Long-Term Evolution-Advanced (LTE-A) cellular networks technologies provide the resources required for M2M communication. However, LTE is required to meet M2M communication requirements, such as power management and support for a large number of devices while maintaining the Quality of Service (QoS) requirements for these devices. Resource allocation or scheduling is one of the key challenges facing M2M communications over Long-Term Evolution networks. M2M traffic has unique characteristics; it usually consists of a large number of small data packets, with specific delays, generated by a potentially large number of devices competing on scarce radio resources. In this article, we will present the scheduling techniques on the Packet Downlink Control Channel (PDCCH) in a M2M scenario based on the priority and differentiation between heterogeneous traffic (real time and non-real time) to achieve high spectral efficiency in cellular systems and provide quality of service guarantees for system users. The results obtained show that the FLS downlink scheduler is the optimal solution based on our proposed method which offers more priority to the real time service (video and voip) than the non real time service (best effort).

234

Authors: Bhagirathi Bai Vittal Rao, Hanume Gowda Chandrakanth

Today, Internet is the best place to communicate and share information among the people throughout the world and gives an endless support of knowledge and entertainment. The main objective of Internet technology is to increase efficiency and decrease human effort. With the introduction of Internet of Things (IoT) in the last decade, we have been pushing for ubiquitous computing in all spheres of life. Physically challenged people are also using the Internet with the help of Speech commands (SC). The main objective of this paper is to minimize the effort and increase efficiency of the Voice recognition and IoT based secured automation, which is named as Raspberry pi and IoT based Speech automation (RASP-IoT-SA) technique. This RASP-IoT-SA system has only worked for authenticated users which improves the security in home and industrial automations. This RASP-IoT-SA system consists of four different Process such as secured speaker prediction, voice data transmission, web update and voice data Receiver processing. In this research work, Mel frequency Cepstral Coefficient (MFCC) technique is used for Feature Extraction (FE). Artificial neural network (ANN) technique is used for two different process such as, speaker prediction and speech data recognition (SR). The experimental result shows that the MFCC and ANN based technique provides better results in terms of accuracy, precision, false measure and recall. The RASP-IoT-SA technique delivers 99% of speech recognition accuracy, which was higher than the existing Speech automation technique.

252

Authors: Sayed Ahmed, Romani Farid Ibrahim, Hesham Ahmed Hefny

In emergencies cases, the main problem is how to arrive to an incident location in a minimum time. In such cases, using the classical distance based routing techniques does not always provide the optimal path in the time needed. In this paper, we present an enhanced routing technique which is based on Dijkstra's algorithm and Analytical Hierarchical Processing (AHP). Eight impendence factors that cause latency in emergency situations are analyzed using the AHP technique to give an overall weight for every road in Greater Cairo Region (GCR) road network. Then, the classical Dijkstra’s algorithm applied on the weighted road network to give the best path between any two nodes in the network. The eight impedance factors considered are: Road Length, Travel Time, Road Type, Road Width, Velocity Limits, Traffic Volume, Mass Density, and Junction Delay. The suggested routing approach is implemented as a mobile application, and a comparison with the classical Dijkstra’s algorithm is performed. The results show that the enhanced routing technique is more reliable than the classical one and more suitable for emergencies situations.

271

Authors: Tipirneni Venugopal, Vusthikayala Siva Kumar Reddy

In this research study, a multi-purpose image water-marking approach was developed for delivering self-recovery, ownership verification and tamper localization of the cover image. For robust watermarking, color and grayscale images were used to deliver wide applicability and generalization to proposed work. Initially, the cover image was converted into wavelet domain by using Integer Wavelet Transform (IWT) and then the logo image was encrypted by using chaotic logistic mapping. This procedure makes the digital image free from false positive error and also helps to provide decent capacity. After encryption and transformation, Least Significant Bit (LSB) was used to provide self-recovery feature as well as to locate the tempered region in the digital image. Then, asymmetric encryption algorithm: Rivest–Shamir–Adleman (RSA) was utilized to improve the hidden data security with high computational efficiency, and good embedding capacity. Finally, the experimental outcome shows that the proposed approach delivers a high secure network with low computational complexity by means of entropy value and Peak Signal-to-Noise Ratio (PSNR). The proposed approach improved PSNR value upto 19 dB and entropy value upto 0.07-0.7 compared to the existing methods: Discrete Cosine Transform (DCT) + Arnold transform and differential evolution algorithm.

292

Authors: Sumalatha Sunkara, Gollamandala Udaykiran Bhargava

A good orthogonality is provided by the wavelet -based orthogonality frequency division multiplexing (W-OFDM) and well reduction of bit error rate is gained with W-OFDM over conventional OFDM systems. Most importantly, it provides enhanced spectral efficiency since it does not require cyclic prefix (CP) which leads to the achievement of ultra-high data rates. Hence, this can be used in 5th generation (5G) wireless communication networks. It is proposed to use the discrete wavelet transform (DWT) based OFDM in place of conventional discrete fourier transform (DFT) based OFDM in the applications of long term evaluation (LTE). However, discrete wavelet transforms (DWT) based OFDM system suffers from the down sampling, which mitigates the actual size of input bit streams and there by degradation in the system efficiency. To resolve this issue, and for enhancing further mitigation in bit error rate (BER) performance, a novel W-OFDM is proposed. Our proposed W-OFDM system utilizes translation invariant wavelet transform (TIWT) in place of DWT for enhanced spectral efficiency. In addition, fading channel environment is considered to disclose the effectiveness of proposed TIWT-OFDM system. Extensive experimental results show that the BER performance of proposed TIWT-OFDM is superior to the existing OFDM systems like DFT-OFDM and DWT-OFDM systems.

1

Authors: Chundru Raja Ramesh, Komaragiri Raghava Rao, Gunamani Jena

Clustering is a procedure of finding similar data items (patterns, documents etc.) and then group the similar data together. Items belongs to different clusters are dissimilar data items, generally cluster values are considered as 1 or 0. The clustering process is not appropriate for all the cases sometimes these values are less than one. In practical situations clusters are not crisp, then it is represented as fuzzy. In order to enhance the clustering rate, two appropriate clustering approaches: K-means clustering and Fuzzy C Means (FCM) are considered. These approaches are modified by minimizing the objective function known as squared error function. The experimental research was performed on the publicly available database (i.e. yeast dataset) to validate its clustering performance in terms of accuracy, specificity, sensitivity and execution time. Experimental outcome shows that the proposed technique improves the accuracy in clustering rate up to 1.5-35% compared to the existing methodologies FCM and k-means approaches.

21

Authors: Arjin Numsomran, Thanit Trisuwannawat, Vittaya Tipsuwanporn

This paper presents an adaptive fractional order PIλDμ control for multi-configuration tank process (MCTP) toolbox which aims at demonstrating the problem of reference tracking and cross coupling rejection in multi-input-multi-output system. Moreover, we investigate the cases where the system is in the mode of minimum phase and non-minimum phase configuration. Besides providing theoretical control system analysis and design, we develop the multi-configuration tank process software toolbox for providing the non-linear functions of dynamic models of multi-configuration tank process which is the advantage tool for investigating the performances of the controllers. The software toolbox is developed from discrete state P-file S-function which is operated within the MATLAB environment for providing many non-linear functions of dynamic models of multi-configuration tank process such as multi-input multi-output quadruple tank full-interacting process, multi-input multi-output quadruple tank process, multi-input single-output triple tank interacting process, multi-input multi-output coupled tank interacting process. All actual process attributes are encapsulated in S-function as the input parameters, thus the multi-tank function block can be simply adjusted by specifying the physical properties of the tank system. The study explains about the mathematical model of multi-configuration tank process, nonlinear dynamic characteristic, minimum phase and non-minimum phase configuration, software toolbox features and also describes the design of the adaptive fractional order PIλDμ controller including the performance validation. The results have been illustrated that the proposed controller design scheme can provide the sufficient effectiveness in the performance, stability and robustness. Furthermore, these tests reinforce the usefulness of MCTP toolbox as a complete simulation tool for users to perform an engineering research of multi-configuration tank control system analysis and design, moreover, it contains very useful for validating the control algorithm of multi-input-multi-output system.

48

Authors: Yarragudi Madhu Sudhana Reddy, Ramaswami Sachidanandan Ernest Ravindran, Kakarla Hari Kishore

Retinal fundus images play an important role in the diagnosis of Diabetic Retinopathy. The detailed information of retinal images like Retinal Vessels, Exudates and microaneurysms may be in low contrast and retinal image enhancement helps in the accurate diagnosis of retinal images related diseases. This paper proposes a novel contrast enhancement method to make the low-contrast retinal images more qualitative such that the detailed information will be clearer. For this purpose, the proposed mechanism considers the spatial mutual relationships between the gray-levels of image and makes the gray-levels in the output image not only linear to the gray-levels of input image but also related to the neighboring gray-levels. The proposed approach enhances the image both in global and local fashion. Spatial mutual entropy based contrast enhancement is accomplished for global contrast enhancement and Greedy contrast enhancement is accomplished for local contrast enhancement. Extensive simulations carried out over various low-contrast retinal images of High Resolution Fundus (HRF) image database shows the outstanding performance of proposed approach. And, different from some other fundus image enhancement methods, the proposed method can directly enhance color images.

74

Authors: Halebeedu Subbaraya Suresha, Srirangapatna Sampathkumaran Parthasarathy

Detection of Alzheimer disease using Magnetic Resonance Imaging (MRI) is the most challenging aspect in the field of medical image processing and analysis. In this paper, the proposed methodology has three major steps: image acquisition, image pre-processing and segmentation. Initially, the brain images were acquired from the dataset: Open Access Series of Imaging Studies (OASIS). After image acquisition, image pre-processing was carried out using median filter, it utilized for cutting down the noise and to improve the quality of acquired brain images. Then, segmentation was carried-out using Fast-Independent Component Analysis (Fast-ICA) along with Otsu multi-level thresholding. It was a flexible high level machine learning technique to localize the object in complex template. In experimental analysis, the proposed approach distinguishes the brain MRI tissues: White Matter (WM), Cerebro-Spinal Fluid (CSF), and Grey Matter (GM) by means of Tanimoto index, similarity index, precision, and recall. The proposed methodology improved the Alzheimer tissue detection up to 15-30% compared to the existing methods: Band Expansion Process (BEP), ICA and BEP-ICA in terms of precision and recall.

96

Authors: Mohamed Ribate, Jamal Zbitou, Rachid Mandy, Ahmed Erkik, Mohamed Latrach

This paper proposes a Broadband Power Amplifier (BPA) for L and S bands applications based on ATF13786 transistor, using GaAs process. To improve the broadband performance, an approximate transformation of previously designed lumped elements into transmission lines, and a multi-section quarter wave impedance transformer are used. With neatly design of the broadband networks and bias circuit, a maximum gain of 14.89 dB is achieved across 1.1 GHz  3 GHz. At 2 GHz, the simulated large signal results demonstrate that the designed BPA achieves a saturated output power of 17 dBm, with 1-dB compression point at 4 dBm input power level, and a PAE of 20 %. For the whole bandwidth, the input return loss and output return loss are below than -10 dB. The maximum value achieved by the reverse transmission is – 20 dB over the operation frequency ranges. Considering the broad frequency coverage, the performance of the proposed design compares favorably with the state-of-art.

114

Authors: Chiranjeevi Pandi, Teja Santosh Dandibhotla, Vishnu Vardhan Bulusu

Opinion Target Extraction (OTE) identifies opinionated aspects in review sentences with the goal of achieving Aspect Based Opinion Mining (ABOM). The available review corpuses and datasets for this task contain a large number of opinionated sentences from wider class of products from different e-Commerce applications. This raised the need for constructing the dataset from these corpora for OTE in a new way called distant supervision. This is by selecting the review sentences based on the learned opinionated relations among the terms in the sentence. The obtained review sentences are then labelled and thus dataset is constructed. The attention model based review sentence selection that uses distant supervision has ignored the review sentences that do not contain opinionated aspect terms and treated them as noise. This has reduced the precision in terms of number of extracted aspects. In order to improve the precision of extracted aspect terms, the attention based review sentence selection is replaced with the learned corpus linguistic rules based review sentence selection. This takes into account the non opinionated aspects available sentences and these aspects are treated as neutral opinion targets. Finally, the extracted aspect terms are analyzed for opinion orientations. It is shown that the approach of corpus linguistics rules based review sentence selection outperforms the attention model based review sentence selection with distant supervision.

140

Authors: Aslam Shaik, Ankireddy Srinivasulu Reddy

Power quality disturbances (PQD) are becoming more and more complicated due to the vast growth of power quality oriented applications particularly the power electronic equipment’s. Since the power with less power quality consequences to a huge loss in the electronic devices, there is a need to detect the power quality disturbances more effectively. This paper proposes a new method for PQD detection and classification based on the entropy characteristics of PQD signals and a novel support vector machine algorithm (SVM). The proposed approach develops a flexible entropy based feature selection (FEFS) mechanism to extract the unique characteristics of all types of PQDs such that the detection system can detect more effectively with in less time. Further to increase the accuracy of individual PQDs, this paper proposed Multi Class SVM (MC-SVM). Various experiments are conducted over the proposed work and at every experiment; the performance is measured through Classification accuracy, False Alarm Rate and computation time. Further a comparative analysis reveals the outstanding performance of proposed approach when compared with conventional approaches.

162

Authors: Mona Abdelbaset Sadek Ali, Mohamed Abd-Elfattah

The aim of this research is to develop a hybrid Model (SVM-LOA) for epileptic seizure detection with support vector machine (SVM) and lion optimization algorithm (LOA) to locate the optimum parameters of support vector machines (SVMs) for classification of Electroencephalogram (EEG) signals . The proposed approach attempts to find the best integration of all available features that offers typical epilepsy detection and gives a better classification rate. Furthermore, the discrete wavelet transform (DWT) has been implemented to divide EEG signals into five combinations. Nonlinear parameters have been calculated and used to be the features to intern the SVM classifier that affects the classification accuracy. We used lion optimization algorithm based approach to optimize the SVM parameters. The overall experimental results shown the LOA-SVM classifiers have 96.78% for accuracy, but SVM that have obtained 80.05% for accuracy. Therefore; the SVM-LOA is an efficient model for neuroscientists to detect epileptic seizure in EEG.

183

Authors: Madugundu Neelakantappa, Amjan Shaik

Mobile Ad hoc Network (MANET) is a ubiquitous, infrastructure less and self-containing wireless network. In this networks nodes that are in mobility forms a temporary network without any infrastructure. In MANETs every node acts as host as well as router and therefore communication can be established on need basis without use of any fixed network equipment. But in presence of malicious nodes, this network leads to major security concerns as these nodes may disrupt the process of routing. In this scenario, detecting or preventing malevolent nodes, launching collaborative gray-hole or black-hole attacks is a major challenge. In this paper, a new secured routing protocol referred to as “Malicious Node Detection by Collaborative Bait (MNDCB)”. This protocol can be regarded as hybrid protocol as it poses the advantage of both reactive and proactive defending architectures. This MNDCB protocol is defined with a reverse tracking method to detect the malicious nodes and there by defending their collaborative attacks. Simultaneous results proves that in the presence of malevolent node attacks, the MNDCB gives better performance over their best counter parts base DSR, 2-ACK and best effort fault tolerant (BFTR) routing protocols in-terms of performance metrics: packet-delivery-ratio, throughput and routing overhead.

206

Authors: Bhavika Gambhava, Chandu Bhensdadia

Deployment of wireless links (terrestrial and satellite) along with wired links has made extension of the Internet even in remote places feasible. TCP/IP protocol suite is an integral part of the Internet. Congestion control of TCP plays a vital role in the performance of the Internet. TCP’s unconditional flow control in case of a packet loss has always been a concern for researchers. Further, halving congestion window in such conditions without taking in to account the current network state is also considered inappropriate. The problem is compounded in wireless networks where packet losses occur often due to channel errors rather than the shortfall in the available bandwidth. In this situation, TCP’s conservative behaviour underutilises the bandwidth. We therefore, propose a scheme to address the issue of underutilization of network resources. The proposed approach, Discrete TCP (DTCP), differentiates slow start and congestion avoidance phases while tuning data flow over a transport connection. DTCP evaluates ssthresh and cwnd before setting up parameters, based on the existing network condition to enhance the performance. The proposed scheme is compared and analyzed with various existing schemes with the help of extensive simulations using ns2. Results of simulation based experiments indicate significant performance improvement of DTCP on erroneous links and in heterogeneous networks and confirm its suitability.

226

Authors: Yassir Rochd, Imad Hafidi

With the blasting growth in data, uptake data mining techniques to mine association rules, and then find useful information hidden in large data has become ever more important. Several existing data mining techniques often through mining frequent itemsets draw association rules and get to relevant knowledge, but with the rapid arrival of the era of big data, traditional data mining algorithms have been impossible to meet large data's analysis needs. Lately, the PrePost algorithm has been suggested, a new algorithm for mining frequent itemsets based on the idea of N-lists. PrePost in most cases outperforms other present state-of-the-art algorithms. In mind of this, we present the HPrePostPlus algorithm. A better version of PrePost based on Hadoop, that utilization a HashMap to traverse effectively the PPC tree, and improve the process of creating the N-lists related with 1-itemsets. We combine also the characteristic of Hadoop with a view to process large data. Experience has demonstrated that HPrePostPlus algorithm is greater than the state-of-the-art methods in terms of performance and scalability.

246

Authors: Saptarshi Gupta, Rattan Lal Sharma

In this paper, a miniature improved gain Circular Patch Antenna (CPA) is reported. An array of Circular Split Ring Resonators (CSRRs) is loaded into a circular patch to transfigure the surface current distributions leading to miniaturization and significant gain enhancement. To obtain a bi-conical beam, the antenna is operated at Higher Order Mode (HOM). The HOM is excited by suitably selecting the feeding location. The unit cell of the CSRR is analyzed and the permittivity of the CSRRs loaded antenna structure is extracted using Scattering Parameter Inversion (SPI) method. The proposed antenna operates in 5.2 GHz band and built on a thin (0.254 mm) RT/Duroid 5880 substrate thus, flexible in nature. The bending analysis is performed showing a natural frequency shift. To validate the proposed design, the prototype is fabricated showing good agreement amongst simulated and measured results is found.

265

Authors: Geetha Megharaj, Mohan Kabadi

Load Balancing is an important aspect of cloud service centers for optimizing the resource utilization. Consumption of excess power in cloud centers can result in monetary wastage. It is critical that, the resources in the cloud center are utilized optimally; so that, both monetary savings and client satisfaction can be achieved. One of the most popular techniques to achieve load balancing is the Virtual Machine (VM) migration technique; wherein, some of the VMs from overloaded Physical Machines (PMs) are migrated to lightly loaded PMs; however, this technique requires excessive time and monetary cost. Recently, a load balancing technique which migrates VM tasks instead of the actual VM was proposed in the literature. This technique was able to overcome some of the limitations of VM migration technique. Here, the overloaded VM does not accept any new task; however, the new tasks are migrated to lightly loaded VMs. Even though this technique migrates extra tasks to achieve VM load balancing, the already overloaded VMs are not relieved from their existing task burden. If some of the existing and suitable tasks in overloaded VMs are migrated, it could improve efficiency of load balancing. In this work, a new run time VM task migration technique is proposed, which migrates tasks from overloaded VMs. The suitable tasks for migration are selected through a discriminant function, which identifies heavy resource consuming and limited execution progressed tasks for migration. Since, it has been shown in the literature that, optimal task-resource mapping is NP-hard, Particle Swarm Optimization (PSO) based solution search technique is proposed. This proposed technique substantially reduces computing load, and achieves good power/energy conservation in the overloaded VMs, when compared with the contemporary VM task migration and VM migration techniques.

290

Authors: Otmane Zebraoui, Mostafa Bouzi

In this paper, we present a modeling and simulation study of a hybrid energy system for isolated sites, which combines photovoltaic system with wind turbine based on permanent magnet synchronous generator (PMSG). With the aim of improving the performances and extracting the maximum energy produced by the two Photovoltaic (PV) and Wind sources, we developed a maximum power point tracking (MPPT) controller with robust algorithms based on the nonlinear sliding mode control (SMC). Thus, other conventional MPPT methods have been simulated and evaluated to compare them with the proposed algorithms in order to deduce the most efficient in terms of the maximum power reach and the system behavior during optimal power point tracking. And for a better analysis study, the simulations are carried out under variable weather conditions. The simulation results show that the proposed robust sliding mode control techniques (RSMC) are more efficient and have better performance than other MPPT algorithms.

314

Authors: P. Vo Ngoc, T. Vo Thi Ngoc

Sentiment classification has been used in many different fields because it has many significant contributions in everyday life, such as in political activities, commodity production, and commercial activities. We have proposed a new model for big data sentiment classification by using a combination of an unsupervised learning algorithm of a machine learning with a Ruzicka Coefficient (RC) in this work. A Self-Organizing Map Algorithm (SOM) of the machine learning is used in clustering the documents of the testing data set (TES) comprising 7,500,000 documents, which are the 3,750,000 positive and the 3,750,000 negative in English, into either the positive group or the negative group of our training data set (TRA) which is 3,000,000 sentences including the 1,500,000 positive sentences and the 1,500,000 negative sentences in English. In this study, we do not use a vector space modeling (VSM). We do not use any multi-dimensional vectors according to both the VSM and many sentiment lexicons. We use many sentiment lexicons of our basis English sentiment dictionary (bESD). We use many one-dimensional vectors based on the sentiment lexicons. We use a similarity coefficient in this study. We do not use any one-dimensional vectors based on the VSM. We have achieved 88.64% accuracy of the TES. The execution time of the proposed model in a distributed network environment - DNE is less than that in a sequential system - SS. Many commercial applications and surveys of the sentiment classification can widely use the results of the proposed model.

1

Authors: Preeti Ivy Barrow, Swathi Jamjala Naryanan

While the genome sequences of different crops have been published and annotated, relatively little is known about the transcriptional networks that regulate gene expression. There are different ways of finding significant motifs in genome sequences, evolutionary motif finding algorithms being one of them. The work presented in this paper uses Differential Evolution (DE) Algorithm to discover new motifs in rice genome sequences and aims to provide an intuitive wizard to analyse the micro-array analysis of expression patterns (motifs) for the queried genes. The used DE algorithm involves crossover operation with fine tuning. Further, to search for already known motifs parallel version of adaptive hash based pattern matching algorithm has been implemented. The study is conducted on rice genome sequences for twelve chromosomes each consisting of approximately 70000 genome sequences. In this paper, experimental results provide a comparison of serial and parallel version of the algorithm applied for the same dataset which shows that parallel implementation improves the performance by almost 25-30% to search the patterns in genome sequences and provide a list of newly discovered significant motifs.

20

Authors: Sribindu Sattu, Moparthy Gurunadha Babu

In cognitive radio systems; primary clients can use auxiliary clients as agreeable transfers to expand their transmission rates while auxiliary clients will consequently acquire more range to get circumstances, prompting coordinated cognitive radio systems. Earlier research proposals in Coordinated Cognitive Radio Networks (CCRNs) predominantly concentrate on giving omnipresent access and high throughput primary client for clients yet have infrequently contemplated vitality proficiency. Moreover, most existing works expect that auxiliary clients are inactively chosen by primary clients, paying little respect to auxiliary client’s eagerness to help, which is clearly not feasible. To address energy issue, this paper identifies an Energy-Efficient Cooperative Strategy (EECS) by leveraging temporal and spatial diversity of the primary network. In particular, auxiliary clients with delay-tolerant packets can proactively settle on the agreeable choices by together thinking about primary channel accessibility, channel state data, primary client’s movement stack, and their own particular transmission necessities. Principle challenge for this cooperative relay determination issue is to choose a relay effectively and economically. Since the quantity of auxiliary clients is expanded step by step because of the quickly developing number of wireless communication gadgets, it is unfeasible to examine all the auxiliary clients and after that pick the best. Predominantly primary client transmitter successively watches auxiliary clients. An arrangement of auxiliary clients is chosen in view of transmission criteria. The hand-off choice plan selects the best hand-off from an arrangement of accessible transfers which are chosen in light of transmission criteria of coordinated connections. In the observation of a hand off primary client need to settle on choice in regards to whether to stop its observation and to pick that as best hand-off or to skip to see next transfer. This problem is addressed by optimal stopping theory and optimal stopping rule. To evaluate the performance of proposed scheme Optimal Selection Approach (OSA), it is compared with auxiliary client’s energy efficient and existing cooperative schemes. Extensive simulation study is conducted so that impacts of different system parameters are investigated and algorithm proposed can satisfy different system requirements.

42

Authors: Jeong-Su Kim, Jeong-Beom Kim

The challenge to solve in large capacity ternary content addressable memory (TCAM) is the large power consumption of search-line and match-line. In this paper, we propose a circuit that uses match-line selective-charging scheme and low-swing search-line scheme simultaneously to reduce power consumption of search-line and match-line which occupies a large ratio in power consumption of TCAM. The circuit was verified by Hspice simulation using a 1.8V supply voltage in a CMOS 0.18 um CMOS process. The proposed 128x128 bit TCAM shows 45.4% power savings in match-line and 74.7% power savings in search-line compared to the conventional precharge high scheme in search cycle. Also, the minimum search cycle time is 3 ns (333MHz).

61

Authors: Nandoori Srikanth, Muktyala Siva Ganga Prasad

Due to shortage of power source, energy is the significant concern part in Wireless Sensor Systems. Wireless Sensor Networks (WSNs) are currently used in numerous kinds of WSN simulators have been established to visually illustrate the coverage, energy consumption status and expected lifetime of WSN sensors. Most of the Researchers have proposed and develop routing algorithm to increase the network lifetime. Among them clustering is the best technique and it is well known for accomplishing energy effectiveness in WSNs. In this paper, Energy Efficient Multi-hop Routing Clustering Protocol (EEMRCP) based on Fuzzy K-Means and Centralized Mid-point Algorithm (FKM-CMA) is proposed for network lifetime improvement. An existing and frequently used algorithm is K- means algorithm in WSN. The main limitation of this K-means algorithm is random initial centroid selection. To improve K-means algorithm, a Midpoint method in initial centroid selection is used. This is named as FKM-CMA. There are two main considerations for cluster head selection in proposed work, one is it’s residual energy, and another one is Euclidean distance used in basic Fuzzy K-means algorithm. Finally, Multi-hop communication is performed for transmitting the packets from CHs to Base Station depending on the distance between them. Furthermore, stimulated outcomes showed that the total network performance of the proposed approach is improved than the other existing approaches.

88

Authors: Udaya Dayanandan, Vivekanandan Kalimuthu

Software quality assessment at the architectural level is efficient because it provides various advantages to find and fix design errors at the earliest stage of software development life cycle (SDLC).Numerous methods have been evolved to validate quality-related issues at the architectural level. The software quality comprises of a total of eight attributes where maintainability is one of the important attribute which affects the overall system quality. Though few methods are available for maintainability analysis, none of the state of art method employs fuzzy logic with Quality Model for Object Oriented Design (QMOOD) metric suite. In this paper, the fuzzy analytic hierarchy process (FAHP) method is proposed with Buckley method to evaluate the influence of maintainability and its sub-attributes in software quality at the architectural level. The proposed evaluation model is tested against several versions of the MFC and OWL windows application frameworks and the obtained results are compared with the state-of-the-art methods. This experimental analysis shows that the FAHP with Buckley method is superior to the other compared methods.

107

Authors: Hemanth Somasekar, Kavya Naveen

Text categorization means dividing a set of input documents into the two or more classes to which these documents belong. Because of increase in availability of data in digital form in large amount, it becomes necessary to organize it. Feature extraction is the crucial step in text classification. Most of the existing text classifiers are lacking in finding out the relations among the terms. In this research work proposed text clustering of Improved Markov Clustering Model (IMCM) and text classification in which the nonlinear relations among the terms are also considered. This approach is scalable to huge dataset also and its classification power is not affected if relations among terms are large. The proposed IMCM method improved the text document clustering significantly and helped to avoid the human interaction in text clustering. Experimental results have shown that proposed system is 91% accurate for 8 categories and decreases, as increase the classes from 8 to 12, from 96% to 94%, respectively. Also, compared proposed model with existing Naive Bayes and k-Nearest Neighbor classifiers. Experimental results show that the proposed model is more accurate than these two classifiers. The better results demonstrated that presented system is developed effectively.

128

Authors: Mallaiah Vorsu, Vinaya Babu Arramraju, Sujatha Bhanothu

Object Oriented Program (OOP) provides the way to reuse the program by pre-implementing functionalities in the software. It is difficult to develop the object oriented software, which is important in the computer programing. For OOP, modularization mainly depends on the class. There are many methods present for the assigning responsibilities, but most of them are based on the human for decision making. In this research, back propagation neural network (BPNN) was used to provide the solution to object oriented design of the software. The Cinema Booking System (CBS) was taken as the input documentation and Formal Concept analysis (FCA) then found the relationship of the element in the lattice manner, after that the relationship was set with each other. The result showed that the proposed system outperformed the existing system and also the design made manually.

147

Authors: Mohammed Lahraichi, Khalid Housni, Samir Mbarki

Object tracking is a crucial step in video analysis, as well as detection and recognition. Particle filter, based on color histogram, is considered among multiple approaches that prove their effectiveness in this domain. It is apparent that if an object and its background, or more objects to be tracked, having nearly the same color histogram, the quality of tracking will be negatively affected. Therefore, combining color histogram and texture features for pixels in motion is considered a potential solution to this issue. In this paper, we propose a particle filter tracking method consisting of two stages: at the prediction stage, the state space model is represented by Box–Muller transform, and at the correction stage, the observation likelihood model is calculated in three steps: firstly, the background subtraction is used to extract the moving objects in order to reduce the impact of background pixels on color histogram. Secondly, the texture Gabor features are computed using moving pixels to distinguish between objects having the same color histogram. Finally, the histogram and texture features are combined to define a likelihood observation model. The proposed method is implemented using several video sequences. The visual and numerical results obtained are compared with those of traditional particle filter method, and show that our method is very efficient particularly in case of similar objects, and clearly improves the quality of tracking.

167

Authors: Pushpa Mohan, Kiran Kumari Patil

One of the most experimentally difficult problem in the world is weather forecasting, which is a basic mechanism in meteorology. Especially in data mining system, there are different information mining strategies are available, for example, K-Means, Artificial Neural Network (ANN) and Support Vector Machine (SVM), etc. These weather predicting strategies are financially high and also very inconsistent for large datasets. To overcome these issues, an effective dimensionality reducing strategy: Self Organizing Map (SOM) is proposed along with Latent Dirichlet Allocation (LDA). The SOM strategy is one of the proper dimensionality reducing strategy to highlight the self-arranging outline. After reducing the measurement, the dimensionality reduced information are used to forecast climate for a reasonable outcome. A reasonable season for an appropriate crop is arranged with the guide of Deep Neural Network (DNN) classification system. This research work depends on finding appropriate information model, which helps in accomplishing high precision and simplification for value forecast. Finally, the experimental outcome shows that the proposed approach improved accuracy in weather and crop prediction up to 7-23% compared to the existing methods.

188

Authors: Pranai Kungwalrut, Viriya Kongratana, Thanit Trisuwannawat, Vittaya Tipsuwanporn, Arjin Numsomran

Optimized water management strategies are among the most crucial concerns in a biomass power plant. Especially during the times of water scarcity, an improper water supply planning and operation result in a detrimental power generation or shut down processes. This paper aims to propose the cyber-physical cloud computing (CPCC), a mechanism of analysis and control physical process using cloud-based framework, in order to improve the efficiency of water management system in a biomass power plant. In this study, a 9.9 MW biomass power plant is implemented to investigate the performance of CPCC. The architecture of the proposed system consists of three physical tiers. The first tier is a tier of physical devices included with pressure, flow, pH sensors, water pumps and valves that are responsible for detecting the physical data and interacting with the water production process. The second tier is an edge computing device which functions as a controller, embedded server, data storage, gateway and switch to manage all operational tasks in an intranet area. The third tier is a cloud computing system which enables big data applications such as online monitoring and visualization of process operation, adaptive filtration fouling control, consuming water and total water cost analysis. The results validate the effectiveness of the proposed system as the ability of an adaptive fouling control system to adjust backwash scheduling and chemical dosing so that achieving the target of purified water quality even during the fluctuation of raw water qualities. Subsequently, the water treatment system can achieve the capability for optimal total water cost operations under a target permeate flow rate and the percent of the recovery. Significantly, the data analytics in the CPCC reflect current operational requirements of water under local climate conditions, likewise contribute to the practical solution for sustainable water resource planning in the energy production.

212

Authors: Mylam Chinnappan Babu, Sankaralingam Pushpa

Discrimination is one of the most important challenging tasks in web mining due to its many legal and ethical features in social media and enterprise based industries. There are an enormous amount of anti-discrimination measures available to prevent discrimination such as using some features like race, religion, gender, nationality, disability, marital status, and age besides situations like employment and training, access to public services, credit, insurance, etc. Practically, those systems are not possible to use in industries due to large datasets. Indirect discrimination contains a set of rules or techniques which are not explicitly specifying discriminatory features, deliberately or accidentally and could create unfair decisions. Existing systems have low classification accuracy and data loss with high discrimination data detection time. To overcome these limitations, an Efficient Discrimination Prevention and Rule Protection (EDPRP) approach has been proposed for removing the discrimination and protects the rule without damaging the data quality. The proposed system designing pre-processing discrimination prevention approach and specify the different features and represent to deal with direct or indirect discrimination. EDPRP is capable of preventing Indirect and direct discrimination, and it allows automatic and routine collection of large amounts of data from the public. In EDPRP, the discrimination prevention model is based on partial data sets as part of the automated decision making. Based on Experimental evaluations, proposed method improves 8% (percentages) of Support and 8 ms (milliseconds) of Execution Time compared than existing methods.

231

Authors: Doaa Elsayed, Eman Nasr, Alaa El Din Ghazali, Mervat Gheith

Web Service Composition (WSC) has attracted considerable attention and research to support Service Oriented Architecture (SOA). WSC constructs complex applications by combing atomic Web services together to achieve users’ requirements. The selection of the best Web service that fulfils the Functional Requirements (FRs) and optimizes the Quality of Service (QoS) requirements, such as response time, cost, reliability, etc., is a critical part of WSC, especially in a dynamic environment. Since requirements frequently change, this demands that WSC must be adapted to provide the most suitable composite services and fulfil the users’ requirements that emerge. This paper presents a new hybrid approach for dynamic optimization of WSC using Parallel Genetic Algorithm (PGA) based on Q-learning, which we integrate with K-means clustering. We call it PGAQK. Q-learning is utilized to generate an initial population to enhance the effectiveness of PGA. K-means clustering is used to cluster Web services based on a fitness function. It is applied in the mutation operator, and used for Web service substitution to prune the Web services in the search space to find the best Web services for the environment changes that might occur at runtime. To the best of our knowledge such a hybrid approach has not been used for WSC before. We implemented PGAQK over the .NET Framework using C# programming language. A series of comparable experiments were carried out showed that PGAQK outperforms traditional PGA and Q-learning approaches in terms of fitness values. PGAQK allows WSC to modify itself dynamically and achieve a better fitness value to fit the changing environment, where the characteristics of composite web services continue to change.

250

Authors: Deepak Nandal, Om Prakash Sangwan

This paper estimates the effort for software by optimizing the COnstructive COst MOdel (COCOMO) model parameters using hybrid BATGSA (Bat inspired Gravitational Search Algorithm) algorithm. The performance of the COCOMO model completely depends upon its parameters which can be optimized by using meta-heuristic algorithms. This paper uses hybrid BATGSA algorithm which hybrids the improved bat algorithm with the gravitation search algorithm (GSA) to optimize the COCOMO model. The bat algorithm demonstrates the hunting and routing behavior of the bat which is improved by using a random walk in the exploration phase. The exploration phase is further improved by using GSA as gravitation force affects the velocity of the bat. The algorithm has been analyzed on four NASA datasets downloaded from promise repository. The comparison of the algorithm has been made with existing three states of art techniques i.e. COCOMO model, BAT algorithm, Improved BAT(IBAT) algorithm by using normalized error as a parameter. The reduction in error ranges from 2% to 10% on different dataset as compared other state of art algorithms proves the significance of proposed algorithm.

275

Authors: Mukesh Bansal, Chaitanya Purushottam Agrawal

This paper develops a technique by using Jaya algorithm and feed-forward neural network to determine the quality of object-oriented software by using Chidamber & Kemerer (CK) along with Li & Henry metrics. The technique basically focuses on the maintainability factor of software quality which in turn depends upon the software complexity. The software complexity is directly proportional to the number of changes done per class which is determined by the technique. The analysis has been done on UIMS (User Interface Management System) and QUES (Quality Evaluation System) datasets by using the mean absolute error as the analysis parameter. The reduction in the mean absolute error as compared to the existing state of art techniques along with the individual component of proposed technique proves the significance of the technique.

293

Authors: Maddela Vijaya Lakshmi, Pardhasaradhi Pokkunuri, Boddapati Taraka Phani Madhav

A coplanar waveguide fed asymmetric ground frequency reconfigurable antenna is designed and analyzed in this paper. The proposed antenna is occupying the compact dimension of 50X45X1.6 mm on FR4 substrate with dielectric permittivity 4.4. The designed antenna is operating in the wideband from 2 to 5.8 GHz with coverage in applications like Bluetooth, LTE, Wi-Fi and WLAN. The reconfigurable nature of the antenna is analyzed with the switching of PIN diodes placed on the antenna structure. The on and off conditions of the diodes D1 and D2 providing switching between the LTE, Wi-Fi and WLAN bands with good impedance matching. The advantage of PIN diodes includes high power handling capability, very low driving voltage and extremely low cost. Here the switch placement and control/biasing strategies depend on the type of the switch and the topology of the antenna. The prototyped antenna with biasing is providing excellent correlation with High Frequency Structure Simulator (HFSS) and Computer Simulation Technology (CST) simulation results. The reconfigurability of the antenna and its parametric optimization is analyzed and presented in this work.

1

Authors: Priti Sharma, Uma Bhardwaj

Spam email is one of the biggest issues in the world of internet. Spam emails not only influence the organisations financially but also exasperate the individual email user. This paper aims to propose a machine learning based hybrid bagging approach by implementing the two machine learning algorithms: Naïve Bayes and J48 (decision tree) for the spam email detection. In this process, dataset is divided into different sets and given as input to each algorithm. Total three experiments are performed and the results obtained are compared in terms of precision, recall, accuracy, f-measure, true negative rate, false positive rate and false negative rate. The two experiments are performed using individual Naïve Bayes & J48 algorithms. Third experiment is the proposed SMD system implemented using hybrid bagged approach. The overall accuracy of 87.5% achieved by the hybrid bagged approach based SMD system.

20

Authors: Kundala Sandeep, Eswaran Parthasarathy, Lakshmi Prathibha

Vision-guided robotic operation is one of the new concepts in teleoperated Unmanned Ground Vehicle (UGV) for military applications. The objective of the Visual Servoing (VS) is to control the position of the robotic arm using the data such as the distance of an object from the reference frame, or the length and width of the object extracted from the vision sensor. In Image-Based Visual Servoing (IBVS) scheme, position control values are computed from image features directly. This work proposes the technique of shadow removal of the object through the advanced light model from the image, acquired using a monocular camera. The 2D spatial coordinates and Point-of-Contact (PoC) of the object with reference to the ground plane is computed using straight-line equations. PoC analysis is made through mapping of pixel distances to spatial distances to analyses mean pixel distance, and the percentage of error obtained between actual distance and calculated distance computed. Other parameters like the width of the object and, the distance between the camera and object is also estimated. The computed result yields a maximum error of 3 cm and shows 2.6% error for camera fixed at the height of 160 cm with a tilt angle of 50˚, which covers an area of 150 cm x 120 cm.

37

Authors: Chairani Fauzi, Selo Sulistyo, Widyawan Widyawan

The environmental adaptability of the smart building is required for energy efficiency. The scheduling-based control model that widely implemented, have a user dependence and assumes maximum occupancy regardless of the occupant's desire in the energy usage requirement during the activity (i.e., ignoring user preferences). In this paper, we present our study on a building control model based on user preference, presence, location, and activity. We present it in a formal model, including conflict resolution techniques on multi-user preference (minimum, maximum, and average preference models). The contribution of this study is the optimization of control model for energy efficiency that also meet multi-user preference. The evaluation of the proposed control model is done through simulation and is compared with the scheduling-based control models. The results show that the minimum, maximum, and average preferences have an energy consumption of 73.5 kWh/day, 44.5 kWh/day, and 58.9 kWh/day, respectively, which are more efficient than the scheduling-based control model. If the Euclidean distance is used to estimate the error value between temperature and light actuation to multi-user preference, the lowest error is the average preference.

59

Authors: Shailendra Singh Gaur, Amar Kumar Mohapatra, Brijesh Kochar

Wireless Sensor Network has various security issues like authentication of sensor nodes and its power consumption. Therefore, we are focusing on evaluation and optimization of power consumption in wireless sensor networks using light weight cryptographic techniques. This research paper studies the certificateless cryptographic algorithms and hence develops an optimal algorithm for the same. The proposed algorithm is more efficient and secured by eliminating the concept of digital signature to make the light weight cryptographic algorithm using PKC and IBE. The simulation result shows that the proposed algorithm can achieve approximately 5-20% reduction of energy consumption with same level of security.

79

Authors: Luther Alexander Latumakulita, Tsuyoshi Usagawa

“Bidik Misi” (BM) is a famous scholarship offered by Indonesia Government which has two main qualification aspects: economic needs and academic performance. Since BM scholarship was initiated in 2010, the number of candidates has increased year by year despite limited quota, causing difficulties in selecting the most qualified candidates. This research is proposing a new selection model by combining Back-Propagation Neural Network (BPNN) as candidate’s predictor and Fuzzy Inference System (FIS) as candidate’s selector. BPNN is used to classify candidates into two, and three recommendation classes then perform FIS on priority set of candidates which is derived from the intersection of classification’s result of two and three recommendation classes to make candidate’s ranking. Closed test results show that for assuming quota of 50% from total 625 candidates or quota = 312, system can choose 267 highly recommended and reject 268 non-recommended candidates while 45 are identified as moderate recommended, and 45 other are identified as candidates of lower priority with selection accuracy of 85.6% better than five previous similar works. The open test results provide accuracy average of 86.88% and the lowest accuracy of 84% which is close enough to the closed test’s accuracy of 85.6% with the difference around 1%, and a small standard deviation value of 2.37 indicates stability of system performance in the selection process. The lowest and highest accuracies of the open test results indicate the proposed system can predict and select BM scholarship's candidates in the future with satisfactory accuracies from 84% up to 90%; thus the proposed system can be trusted as a proper model for BM scholarship selection process.

100

Authors: Mandhula Trupthi, Suresh Pabboju, Gugulotu Narsimha

Social media are generating an enormous amount of sentiment data in the form of companies getting their customers’ opinions on their products, political sentiment analysis and movie reviews, etc. In this scenario, twitter sentiment analysis is undertaken for classifying and identifying sentiments or opinions expressed by people in their tweets. Usually, the raw tweets consist of more noises in terms of URLs, stop-words, positive emojis and negative emojis, which are essentially reduced. After pre-processing, an effective topic modelling methodology Latent Dirichlet Allocation (LDA) is implemented for extracting the keywords and identifying the concerned topics. The extracted key words are utilized for twitter sentiment analysis using Possibilistic fuzzy c-means (PFCM) approach. The proposed clustering method finds the optimal clustering heads from the sentimental contents of twitter-sanders-apple2 database. The acquired results are obtained in two forms such as positive and negative. Finally, the experimental outcome shows that the proposed approach improved accuracy in twitter sentiment analysis up to 3-3.5% compared to the existing methods: pattern based approach and ensemble method.

121

Authors: Margaret Sangeetha, Velumani Padikkaramu, Rajakumar Thankappan Chellan

Data clustering is one of the active research areas, which aims to group related data together. The process of data clustering improves the data organization and enhances the user experience as well. For this sake, several clustering algorithms are proposed in the literature. However, a constant demand for a better clustering algorithm is still a basic requirement. Understanding the necessity, this paper proposes a density based clustering algorithm which is based on Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The main drawback of DBSCAN algorithm is it requires two important parameters as initial input. It is really difficult to fix the values for these parameters, as it requires some prior knowledge about the dataset. This requirement is overthrown by the proposed clustering algorithm by selecting the parameters automatically. The automated selection of parameters is achieved by analysing the dataset and it varies from dataset to dataset. This way of parameter selection improves the quality of service and produce effective clusters. The experimental results show that the proposed approach outperforms the DBSCAN algorithm in terms of purity, F-measure and entropy.

138

Authors: Kavita Arjun Sultanpure, Lakkam Siva Shanker Reddy

Virtualization is used for saving energy, cost and is run for multiple applications using various operating systems on the same server at the same time. Thus, it increases the CPU utilization, efficiency and flexibility of the computer hardware. This paper presents the concept of job scheduling for energy efficiency using Artificial Bee Colony (ABC) by utilizing virtual migration concept in addition to broadcasting of requests. Due to the fact that the virtual migration concept helps a lot in management of the scheduling in cloud computing, so, the validations of job scheduling and service level agreement (SLA) violations with and without virtual migrations are opted in this work. In the initial phase, when server gets the requests for task execution, then the sub-servers (S1, S2, …, Sn), executes that requests (R1, R2,…Rn) irrespective of the main server. When the load at sub- servers increased, then the usage of virtual migration machines is executed, so that the jobs can be implemented equally. Two types of energy has been obtained in the work, first, which is utilized for the execution of the task and second, when virtual migrations creates energy reduction of overall energy (E= E1, E2…En). So, for given interval of time, the creation energy for virtual migrations came out to be less than the allocation energy of virtual migrations for jobs, to have better accuracy. At last, the comparison of proposed work, with existing work is provided. In the existing work, we have considered the papers that evaluate SLA violation and job scheduling by using neural network (NN) and genetic algorithm (GA). The SLA violation of ABC algorithm with respect to GA is reduced by 10% whereas SLA violation of ABC algorithm with respect to NN is reduced by 15%. Job scheduling of ABC algorithm with respect to NN is improved by 15%.

159

Authors: Zeinab Ezz Elarab Attia, Waleed Arafa, Mervat Gheith

The paper presents a new system for correcting short answer questions using the course material in addition to the model answer. Such system overcomes the limitations of the current systems that are just based on calculating the similarity degree between the model answer and students’ answer. Thus they neglect any correct answer that may be written by a student but not mentioned in the model answer. Also, they neglect correcting student answers that contains definition or explanation for a concept stated in the model answer. To overcome such limitations, the proposed system uses the course material to expand the model answer with more correct answers via answering the short answer questions. In addition to expanding the model answer’s concepts with their synonyms and definitions (using is-a relation and have relation). The proposed system is tested and evaluated on Texas dataset. The proposed system outperforms those systems that are evaluated on the same dataset, by achieving a correlation value about 0.8.

177

Authors: Gadda Archana Devi, Muparthy Gurunadha Babu

Due to the higher data transmission networks and maximum efficiency, multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) has become most vital wireless communications system in nowadays. Accurate estimation of channel under severe time and frequency selective fading channels has been required by these MIMO-OFDM systems for enabling high practical mobility. Established methods trust on non-realistic estimations or result in impractical complexities. In this, we presented a new MIMO-OFDM channel estimation (CE) scheme for pilot length minimizing by designing novel pilot bits from the Hadamard matrices of Paley (HM-P) type I allows its orthogonal and diagonal constant matrix structures manipulations. Additionally, we also implemented a method which estimates the channel impulse response length to enamour existed number of multipath within the channel by stretching the length of pilot. Furthermore, our proposed channel estimation scheme modified to derive the initial fluctuation of channel and its Doppler rate, afterwards during the payload applied for tracking of MIMO channel using fast adaptive hybrid linear quadratic estimation (FAH-LQE) filter. Experimental analysis has done in MATLAB environment under Rayleigh distribution and achieved excellent channel estimation and channel tracking performances over the state-of-art channel estimation algorithms. Our proposed scheme also achieved lower diversity of transmitting bits by applying FAH-LQE filter and the superior estimation of channel under multipath conditions while increasing the number of pilot bits sent per transmitting antenna.

196

Authors: Azhagiri Mahendiran, Rajesh Appusamy

The huge proliferation of cyber economy, social media usage and online transactions has resulted in large volumes of data in the cyber space. This has led to an increase in concern over the security of confidential data in the cyber space. Network security situational awareness systems helps in effectively monitoring a network for suspicious activities and thwarting any attacks on the information stored in the network. In this paper, an intrusion detection system for network security situational awareness using conditional random field has been proposed. Conditional random fields being conditional models are capable of modeling inter relationships between the observed features. This results in greater accuracy in classification. Conditional random field’s complexity increases with the number of features in the observation. To reduce this complexity, a feature selection method using oneR algorithm has been proposed. The ability of oneR algorithm to find the best attribute that result in optimal classification has been used for ranking the features in the observation. The proposed system was trained and tested using the bench mark NSL-KDD dataset. The proposed system on experimentation, exhibited higher accuracy (98%) in identifying an attack in general and also showed better performance (>93%) in identifying individual attack categories specifically.

215

Authors: Ashok Kasirayagouda Patil, Siddanagouda Somanagouda Patil, Prabhakar Manickam

Lung cancer is defined as an uncontrolled cell growing in the tissues of lung, which is also said to be lung tumor. The lung cancer is curable in the starting stage, but identifying the lung cancer in starting stage is very difficult. In recent decades, researchers showed great interest on gene level lung cancer identification using shortest path between the lung cancer related genes. Many research has been done to identify the shortest path between the genes, but the conventional methods consumes more time for processing the data. In this research, Protein to Protein Interaction (PPI) structure is constructed from the weighted protein present in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. For identifying the shortest path between the genes in PPI, an effective algorithm: enhanced Floyd warshall algorithm is proposed. Floyd warshall is efficient in finding the shortest path between the genes and also solves all pairs of shortest path problem. A major drawback of Floyd warshall algorithm is, it works slower than other conventional algorithms designed to perform the same task. To improve the performance of traditional Floyd warshall algorithm, an iterative matrix is used for eliminating the invalid path. Then, the comparison between the proposed method and existing system is given in the experimental result. Experimental outcome shows that the proposed approach improved the time consumption up to 2-3 sec compared to the existing methods: Dijkstra’s algorithm and Floyd warshall algorithm.

233

Authors: Yuttana Kongjeen, Wannawit Junlakan, Krischonme Bhumkittipich, Nadarajah Mithulananthan

This paper has presented the estimation methodology of the quick charging station for electric vehicles (EVs) based on both area and population density data. The proportion of EV owners per number of population in location data; is also used to compute the number of the quick charging stations. The population density data and proportion of EVs owners per number of population in area data are varied from 1 to 6 % and 0.01 to 0.8 %, respectively. The simulation results showed that the number of EVs stations increased and the randomly selected Feeder No.1 was installed at EVs stations; of which range from No.1 to No.4. The total real power loss increased up to 18%. Therefore, this study could be verified that the quick charging stations should be considered both optimal in sizing and location of EVs charging stations.

251

Authors: Nagraju Naik Mood, Venkata Subbareddy Konkula

This paper proposes a Secure, robust and intelligent watermarking approach based on Redundant Wavelet Transform, Singular Value Decomposition and Genetic Algorithm. Here the robustness. The proposed watermarking scheme accomplishes RWT and SVD for feature extraction and the GA for optimization. Further this approach proposes a signature embedding mechanism by which the watermarked image will be more secure. The Genetic Algorithm proposed in this approach adopts intelligent property and the signature process adopts more security. Further the combination of RWT and SVD adopts the robustness to the proposed approach. Numerous experiments are conducted over the proposed approach and the performance is evaluated through the performance metrics such as Peak Signal to Noise ratio (PSNR) and Normalized Correlation (NC). The obtained experimental results demonstrates that the proposed approach is superior compared to conventional approach and is able to provide efficient resistance over Gaussian noise, salt & pepper noise, median filtering, cropping, rotation, contrast enhancement, scaling and Histogram Equalization attacks.

271

Authors: Pradeep Krishnadoss1* Prem Jacob

Cloud computing offers dynamic allocation of resources on demand, the feature which makes it to stand apart providing great performance, scalability, cost efficient and less maintenance, thus making it an apt choice. Task scheduling becomes the essential factor in increasing the performance for the dynamic allocation of resources which is most essential in the cloud environment to increase performance and decrease the cost. In this work, a solution is proposed using makespan and cost, taking them as important constraints for the optimization problem. We have merged two algorithms namely, cuckoo search algorithm (CSA) and oppositional based learning (OBL) and created a new hybrid algorithm called oppositional cuckoo search algorithm (OCSA) to provide solution to the above stated issue. Our proposed OCSA algorithm showed noticeable improvement over the other task scheduling algorithms. The proposed work is simulated in cloudsim programming environment and the simulation results show the effectiveness of the proposed work by minimizing cost and makespan parameters. The obtained results are better in comparison to other existing algorithms like particle swarm optimization (PSO), Improved Differential Evolution Algorithm (IDEA) and genetic algorithm (GA).

1

Authors: Santhosh Kumar Gangadharaiah, Hosahally Narayangowda Suresh

Automatic extraction of soft biometric characteristics of face image is an emerging research field in recent years. Among these soft biometrics, age estimation is very useful for several applications, like video surveillance, business intelligence, and search optimization in large databases. Generally, facial aging effects perceived in two main forms like, growth related transformations and textural variation. So, in order to generate an effective age classifier, both dimension and texture information should be used together. In this work, an age estimation system that is combination of dimension feature (Hybrid Principal Component Analysis (HPCA)) and textural feature (Log Gabor filter) for feature extraction, is used. The extracted feature values are classified by employing a multi-objective classifier named as Multi-Support Vector Machine (M-SVM). The result of the experiment shows that the proposed approach delivers a better age estimation rate on overlapping age group classes.

18

Authors: Premkumar Manoharan, Sumithira Rameshkumar, Sowmya Ravichandran3

In the field of power electronics, multilevel inverter plays a vital role for power quality improvement. The high switching frequency is used to obtain quality output waveform with a minimum amount of ripple content. The high voltage is reached by using voltage source inverter with almost zero harmonics without series connected switching devices. If the voltage level increased, the harmonic content of the output voltage waveform decreases in multistage converter. In this present work, single phase cascaded nine level inverter is designed for low powered microinverter and switching angles are produced by Selective Harmonic Elimination (SHE) technique. Each bridge in cascaded inverter will produce the output at different levels and allowing an overall nine level AC output voltage. Now-a-days, the solar photovoltaic is connected with microinverter is an efficient way of increasing the performance so that the proposed inverter derives an input from single PV module since it is module level inverter and it can provide good regulation for leads up to their rated capacity. The prototype is designed for 80W rating and the control signals are generated and processed using FPGA controller. The proposed inverter is designed and tested experimentally to verify the theoretical explanations and proposed design delivers high efficiency when compared with the conventional inverter at full load.

39

Authors: Jayapandian Natarajan

Cloud Computing reshapes the entire computing paradigm. In general, cloud computing means outsourcing available services and data storage in centralized scenario. In cloud computing task allocation is a major problem because multiple numbers of tasks are allocated to multiple numbers of processors for simultaneous processing. From the given list, tasks are queued according to the ascending order based on their duration. This paper is designed to solve the Task Scheduling problem, by using our proposed effective new approach of Backfilling algorithm. Depending upon the task duration, tasks are split into multiple threads for processing. Multiple thread tasks are processed in the basic concept of “gang scheduling” technique. Here we implement new backfilling algorithm concept to minimize the idle processing time of the processors. The existing Simple Backfilling Algorithm (SBA) is used to minimize the ideal time processing. Whereas comparatively Dynamic Cloud Scheduling using Backfilling Algorithm (DCBA) is designed to reduce the ideal time processing than SBA to carry out the process of both LQueue and SQueue simultaneously. At the outset, DCBA reduces the average waiting time. As mentioned the algorithm which is specified in the previous line that contains three level which represent the working speed of the algorithm. The first and second level of DCBA algorithm is comparatively similar to the performance of SBA algorithm. The maximum better performance was given in a queue size (q=1.5) by DCBA algorithm as compare to SBA algorithm. The existing type (Gang Scheduling) consist of two approaches namely Adaptive First Come First Serve (AFCFS) and Largest Job First Served (LJFS) that focus on non-parallel jobs with deadline. When compare to existing gang scheduling algorithm and SBA algorithm the average waiting time of DCBA has slight improvement in the loader level of the key. As the separation of the queue like LQ and SQ the waiting time and average waiting time is reduced comparatively.

57

Authors: Shriraghavan Madbushi, Rajeshree Raut, Mulpuri Santhi Sri Rukmini

In this paper, a novel method for detection of Primary User Emulation Attack (PUEA) by tagging (adding tag bits) a test signal in chaotic communication is proposed. The tagged test signal is masked by the secondary user using Lorenz chaotic attractor and sent over the channel. However, it doesn’t interfere with test signals of other secondary users. The Bit Error Rate (BER) analysis of the received test signal indicates whether the channel is free or is under PUEA. If the BER of the received signal is above the set threshold then it indicates an attack. Moreover, for the first time the concept of test signal to identify whether the channel is occupied by the primary user or an attacker is proposed. It is further suggested to compare the result of the proposed method with the database provided by Spectrum Bridge Inc. (SBI). It is worth mentioning here that the proposed method has made the use of the centralized control approach employed by the Federal Communications Commission (FCC) to combat PUEA. Simulation results in terms of the BER have been analysed and compared with other existing methods to show the efficacy of the proposed method.

76

Authors: Suyambu Karthick

Now a day the usage of Self-Organizing Networks (SONs) is increasing, because of its broad application. Unlike common networks, the SONs have the capability of reconfiguration, whenever any defect occurs in the network. However, while routing in this network, the loss of data occurs due to its security lack. Hence many researchers have presented their research for providing secure routing in various SONs. However, the security is still an issue in the routing of SONs especially in Wireless Sensor Network (WSN). Here in this paper, a novel protocol for the secure routing of WSNs has developed. The proposed protocol is named as Trust-Distrust Protocol (TDP). As per the proposed protocol, the routing is done in four stages. The initial stage is topology management using the k-means algorithm. The second stage is Link Quality Appraisal (LQA), where the quality of every network node is evaluated. The third stage is grading, in which it is based on the LQA value and a grade point is allotted to every node in the network. In the last stage, the most secure path for the routing is determined based on the grade points. The proposed protocol is tested in one of the major kinds of SONs, like WSN using the NS2. By comparing with the existing LEACH protocol, the performance is evaluated. Ultimately the proposed protocol outperformed the performance of the existing routing protocol and suggested for rounding in SONs.

93

Authors: Sadanand Ramchandrarao Inamdar, SathishBabu Basavaiah, Ravi Malleshappa Yadahalli

In Mobile Ad Hoc Network (MANET), the existing routing protocol may result in overhead and increased collisions during data transmission. In order to overcome this issue, in this paper, we propose to design a cooperation based directional routing protocol in MANET. Initially the source nodes discover neighbors by constructing the Directional Neighbour Table (DNT). Then the node with minimum transfer time is selected as best relay node. The transfer time is estimated using bio-inspired algorithm. Once the relay nodes are selected, the source node follows the Adaptive Directional Monitoring MAC (ADMMAC) protocol to transmit the data to the destination. In this technique, the nodes use the co-operative transmission mode when it requires transmitting the data packet faster. By simulation results, we show that the proposed technique minimizes the overhead and enhances the efficiency.

111

Authors: Mohamed Kasim Jannathl Firdouse, Mururgan Balasubramanian

Computed Tomography (CT) is the main source for analyzing the lung diseases and the preplanning of pulmonary surgeries. The accurate interpretation of chest CT scan makes a challenge to radiologists due to its complex visual nature. This paper proposes a novel method to segment the left and right lungs from the background of CT images to assist the pathologists for the easy and accurate diagnosis of pulmonary diseases. The segmentation of lung is a challenging task due to the anatomical difference of different people. This paper develops a novel algorithm namely successive peak based quantization transform (SPQT) to segment the lungs from the background. This method extracts 8 bit patterns from the input lung image and at last a BYTE data which is formed from 8 bit patterns is produced. Prior to SPQT lung segmentation, the lung image which may contain impulse noise is reduced into noise free one by using ‘Fuzzy Impulse Detection and Reduction Method’ (FIDRM). The novel lung image hikes the accuracy to a significant level than the existing algorithms.

132

Authors: Enumula Mahesh, Suman Maloji, Dhyapulai Nagendra Rao

Now a days Image forgery (IF) makes many problems in a society. Super Pixels Segmentation and ScaleInvariant Feature Transform based Image Forgery Detection (SPS-SIFT-IFD) has introduced to detect the forgeries in the images. The forgery region extraction algorithm replaces the features point with small SP as feature blocks and neighboring blocks that have similar local color features has been identified by the Scale-Invariant Feature Transform (SIFT) technique. Artificial Neural Network (ANN) classifier is used for better detection of forged parts in the original images. It applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the SPS-SIFT-IFD scheme achieve much better detection results even under various challenging conditions compared with the existing Forgery Detection (FD) methods. Finally, SPSSIFT-IFD method performance is measure, in terms of Recall, Precision, False Measure, Sensitivity, Sensitivity, Accuracy and Gmean.

152

Authors: Manjula Thimsandra Ramakrishna, Raju Garudachar

Signal tracking loop in GNSS receivers play a major role of data extraction by tracking carrier phase. Under adverse signal conditions such as ionosphere scintillation, conventional tracking loops are less robust and hence are susceptible to loss of lock. Kalman filter’s inherent ability to adapt to signal conditions is one of the desirable features which improve the tracking performance. The paper analyses Linear Kalman filter technique of tracking signals in Navigation receiver under scintillation conditions and presents the improvements obtained with Linear Kalman filter based tracking method over conventional tracking method. Performance parameters such as phase error, carrier to noise ratio, correlator power and phase lock indicator are analysed in addition to highlighting the tuning merits of Kalman filter. NavIC L5 band signal modulated with simulated scintillation is used as test signal to analyse the performance of the Kalman filter based tracking method.

173

Authors: Samuel Peter James I, Ramasubramanian P, Magdalene Delighta Angeline D

The emotions of human in every part of a life are usual and unavoidable. The emotions uplift or cast down a person facing different situations in each moment. The main objective of this research work is to provide learning with emotional-based approach and to analyze the result using data mining algorithms. This paper examine how emotions involve the learners learning and the success of performance improvement of various learners under different environment that leads to the success of a learner. This study scrutinize the Emotional Intelligence (EI) and how it relates to academic performance. The key idea is to bring out different learning environments that focus on the learners’ positive emotional behavior and proceed according to the observed behavior. The proposed work uses Observe, Amend and Analysis (OAA) Method in the learning environment to enhance the learners learning and to evaluate performance using Data Mining Tools to enhance the future outcome. The experimental results show that with the proposed approach the learners learning is improved with positive emotions.

192

Authors: Pushpa Mohan, Kiran Kumari Patil

In India Rice and ragi are the two pre-eminent crops that stable’s the food of eastern and southern part of the country. These two crops mainly grown in rain fed areas, which receives heavy annual rainfall. In this scenario, Mysore district is undertaken for experiment analysis. As the region receives heavy rainfall (782mm). In this region the rainfall rate is high but the productivity of this crops are very low due to irrelevant crop cultivation. In order to overcome this concern, an advanced technology named Weighted-Self Organizing Map (W-SOM) is employed for accurate crop and weather prediction, which is the combination of both Self Organizing Map (SOM) and Learning Vector Quantization (LVQ). In this paper, the prediction accuracy is enhanced by minimizing the Within Class Error (WCE) among the clusters. Therefore, this new approach outcome shows a clear idea about suitable crop cultivation in Mysore. Experimental outcome shows that the proposed approach improved accuracy in crop and weather prediction up to 0.5-2% compared to the existing methods: SOM, Kernel-Nearest Neighbors (KNN) and Ensemble Neural Network (ENN).

208

Authors: Puttaswamy Gowda Vanajakshi, Murugavelu Mathivanan, Thangamuthu Senthil Kumaran

There is a big challenge in speech recognition system due to variability of the spoken languages and also speech signal is degraded due the environmental noise. Therefore speech recognition system requires pre-processing, which plays vital role to restore speech signal more effectively. Many challenges are there to restore the speech from different noisy environment. In this paper, proposed method is divided into two parts. First part is Kannada speech restoration, the modified wiener filter is proposed to restore the Kannada speech signal with very good speech quality. The experimental results are verified for different types of noise by varying input Signal to Noise Ratio (SNR) from -20dB to 5 decibels (dB). Second part is Autocorrelation based Kannada speech recognition system. Autocorrelation technique gives better output SNR for degraded input SNR with -20dB. The Autocorrelation is the simple method to recognize the isolated Kannada word in speech signal. This proposed technique results 100% recognition rate for male, female, with different accents.

225

Authors: Kasarla Satish Reddy, Hosahally Narayangowda Suresh

Reconfigurable Finite Impulse Response (RFIR) filter plays an important role in Software Defined Ratio (SDR) systems, whose filter co-efficient change dynamically during runtime. In this paper, Low Cost Carry Bypass adder Reconfigurable Finite Impulse Response (LC-CBA-RFIR) is introduced to perform the RFIR filter operations. DRAM-based Reconfigurable Partial Product Generators (DRPPG) consists of MUX and dual port distributed RAM, which has co-efficient to perform a FIR filter operation. With the help of Verilog code, the RFIR filter architecture was verified in Modelsim software. The same Verilog code was used to analyse the ASIC performances such as area, power and delay Area Power Product (APP), Area Delay Product (ADP) as well as FPGA performances such as LUT, flip flop, slice and frequency. After implementing ASIC and FPGA, all the performance improved in LC-CBA-RFIR method compared to the conventional methods.

246

Authors: Sunitha Mallasetty Shivamallaiah, Kwadiki Karibasappa

Nowadays, wireless communication plays a major role in sending information from one area to another in an effective way. In wireless communication, the important parameters are coverage area, power consumption, lifetime, and Black Hole (BH) detection in network with interconnected mobile nodes. Ant Colony Optimization (ACO) is used for finding shortest path in the Network. ACO helps in choosing another path, if there is any malicious node in the network. Data is secured using Dual Rivest-Shamir-Adleman (DRSA) technique and Black Hole Detection (BD) is used for identifying malicious node in the network. For efficient wireless system these parameters have been optimized, several algorithms have been applied to optimize the performance parameters and detect the BH in the network. Efficient BH identification with optimized routing algorithm is implemented in secured environment by using Dual RSA. The ACO-DRSA-BD algorithm precisely detected the BH Node (BHN) in finding the proper solution for data transmission using ACO and secured environment using DRSA. Hence, “ACO-DRSA-BD” finds proper solutions for transmitting data in the presence of BH Attacks and enhancing better performance parameters in terms of throughput, routing overhead and energy consumption.

266

Authors: Ammar Djerioui, Dehimi Ouali, Mohamed Ladjal

This paper proposes a sliding mode control strategy for hybrid power system (HPS). The hybrid power system consists of four-leg voltage source Inverter Bridge. The HPS ensures full compensation for harmonic phase currents, neutral current, reactive power compensation and unbalanced nonlinear load currents. The Sliding Mode control strategy with a three dimensional space vector modulation deals with power quality enhancement in standalone power-supply systems with the key objective to compensate for AC side load and phase disturbances that result essentially from the presence of nonlinear and unbalanced loads. The four-leg voltage source inverters play an important role by providing both balanced and unbalanced loads with clean power and balanced output voltage. The simulation results show that the performance of the four-leg HPS with the proposed control algorithm.