International Journal of Intelligent Engineering and Systems

1

Authors: Sreesudha Parnapalli, Barooru Lakshmi Malleswari

In the late years, MIMO MC-CDMA approach has been proposed in order to expand framework capacity over frequency-selective wireless channels. The key component of MIMO MC-CDMA is the capacity of abusing diversity jointly in time, frequency and in the space domain. In this work, we implement an efficient multi-user STBC MC-CDMA transmission System Based on Minimum Conditional BER Criterion and Oppositional krill herd algorithm (OKHA) assisted channel estimation. The multiuser MC-CDMA system using Alamouti’s Space-Time Block coding for encoding the transmitting sequence. The estimation of Channel State Information (CSI) is optimally selected using OKHA. Normally, KHA is a natural-inspired metaheuristic algorithm which mimics the herding behavior of ocean krill individual. To improve the convergence speed and accuracy of the basic KHA algorithm, in this proposed work we combine KHA with oppositional based learning (OBL). The experimental results are conducted for the different algorithm based on BER, SER, and spectral efficiency. The simulation results show that the proposed algorithm is properly reducing the BER, SER and properly increase the spectral efficiency value compare to other techniques.

18

Authors: Gadekallu Thippa Reddy, Neelu Khare

Huge amount of medical data is available today. In order to predict the disease we need a reliable method to diagnose the disease. In this paper we introduce a technique known as FFBAT-ANN prediction algorithm which is categorized as Feature reduction and Diabetes disease classification and such a process is carried out using LPP algorithm and FFBAT- artificial neural network classifier respectively. Initially, LPP algorithm is employed to produce fuzzy rules by recognizing the attributes corresponding to the diabetes disease. Later on, the classification is carried out by blending FFBAT optimization technique with Artificial Neural Network classifier. Finally, the experiment is performed on diabetes dataset. The experimental result proves that the performance of our proposed method outperforms the other conventional methods.

41

Authors: Hady E. Abdel-Maksoud, Mahmoud M. Khater, Shaaban M. Shaaban

Aiming to improve the control performance in the switched reluctance (SR) motor drives, this paper presents a simple nonlinear mathematical model of SRM. The model depends on describing the characteristic data obtained from finite element analysis (FEA) in a simple parametric formula. An adaptive fuzzy logic PI controller is used with the suggested model in speed control drive system. The controller is designed based on Mamdani type with nine control rules along with Gaussian function memberships. The control technique is implemented and tested under different operating conditions as well as its results are compared with those of conventional PI controller. Both control schemes operate in pulse width modulation (PWM) control mode. The proposed adaptive fuzzy logic PI improves the motor speed performance in terms of tracking precision and travel time.

60

Authors: Shounak Rushikesh Sugave, Suhas Haribhau Patil, B. Eswara Reddy

Software testing is required to detect the faults and to ensure the quality of the software under development. Usually, test suites are used to evaluate the software system during the software development cycle. But often test suites contain more redundant test cases due to overlapped test objectives. So, the test-suite reduction is an important step to reduce the number of test cases so as to satisfy the entire objectives with less computational cost. Literature presents different methods to select the suitable test suites of optimal subsets for regression testing. Accordingly, this research aims to develop an effective test suite reduction approach for regression testing. The proposed algorithm (GTAP) is newly designed using TAP measure and greedy search algorithm. This algorithm uses TAP-measure which is specially developed for measuring the importance of test cases. The performance of the GTAP algorithm is evaluated using four different evaluation metrics with eleven subject programs available in SIR repository. From the experimentation, the average performance of the proposed GTAP algorithm in all the programs is 93.07% which is higher than the DIV-GA which obtained the value of 90.27%.

81

Authors: Sharmasth Vali Yeruru, Tiruchirai Ramanujam Rangaswamy

The distinctive aspect of Mobile Ad Hoc Networks (MANETs), including dynamic network topology, susceptible wireless medium, limited battery power, network overhead are highly vulnerable and remains as major issues in designing Intrusion Detection Systems (IDS). This paper proposes a framework of Anomaly IDS with Subjective logic based Trust (AID-ST) system to discern and eradicate the intruders from the network. AID-ST system incorporates multi-dimensional trust parameters and subjective logic theory to effectively detect and confirm the attack behavior. The system measures indirect trust of a node using subjective logic theory, and IDS estimates trustworthiness of a suspected node by collecting and combining the evidence from various observers. The simulation results depict that the AID-ST attains high performance in terms of detection accuracy, overhead, and energy consumption when compared to existing AIPD AODV.

100

Authors: Mahendra Pratap Singh, Sanjay Kumar Dubey

Anaemia is a serious health condition caused primarily by deficiency of iron in the body. In India cases of anaemia are on an alarmingly high rate affecting pregnant women, men and children alike. Malnutrition has been identified as the primary cause of iron deficiency. The treatment of anaemia can be accomplished by adhering to a stringent diet plan containing all the critical nutrients in the right amount. This makes it a complex process to pick the most relevant diet plan among all the available ones. Our objective here pertains to determining the best diet plan for anaemia patient and to procure this we are employing Analytic Hierarchy Process (AHP) in the proposed paper. Diet recommendation for anaemia patient using AHP is relatively new, making this work as unique. The results obtained show the best diet among the available ones that must be recommended to an anaemia patient. This diet contains all the meals that must be consumed at breakfast, lunch and dinner by an anaemia patient. The proposed work is further validated using Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results obtained after validation are exactly the same as determined using AHP.

117

Authors: Fakhriy Hario, I Wayan Mustika, Adhi Susanto, Sholeh Hadi, Sevia M Idrus

Nonlinearity characteristic in OFDM (Orthogonal Frequency Division Multiplexing)-RoF (Radio over Fiber) system which causes the frequency shift of light and has a high electrical field on medium transmission. We begin by discussing SPM (Self Phase Modulation) and GVD (Group Velocity Dispersion) are part of nonlinear characteristic. SPM induced spectral broadening will be degrade performance of light wave and GVD will be impact on duration pulse of optic. The impaired information signal reduces the power and later affects the quality of signal. This research aims to focus on the improvement of two external modulator LiNbO3 MZM (Lithium Niobate-Mach Zehnder Modulator) and applying of frequency dithering techniques to reduce the existing nonlinearity of the optical fiber purposely to enhance the receiving power for the lower input power with the length of fiber up to 100 km. Finally, the result shows an average of 18.5% increase in the power level for linewidth of 1 MHz, 3 MHz, and 7 MHz across 50 km distance with the log BER (Bit Error Rate) -2.407, BER 0.003, and SNR (Signal to Noise Ratio) 43.8, across 100 km of optical fiber the value of log BER -2.471, BER 0.003, and SNR 49.14.

135

Authors: Thaksin Sangsuwan, Teerawat Thepmanee, Amphawan Julsereewong

Detailed knowledge of how to balance between safety and availability for instruments is essential to successful design and implementation in fieldbus-based process control systems. The configuration correctness is crucial to actually obtain the benefits of digital fieldbus technology used. In actuality, a number of device parameters must be set to provide the required functionalities. This paper focuses on improving safety and availability for the proportional-integral-derivative (PID) and cascade control strategies using Foundation Fieldbus (FF) with control in the field. The aim of this paper is to analyze the logical behaviors of PID and cascade loops in two conflicting purposes for studying how different configuration options affect the interlocks between function blocks located in instruments as well as the initialization and fail-safe mechanisms in response to invalid measurements. For safety purpose, the interested control loops are configured to bring the process to a safe state in the presence of a fault. For availability purpose, the interested control loops are configured to keep the process running in the event of a failure. A water tank process is utilized as a case study for control loop implementations. Interactions between the status propagation and mode shedding for demonstrating the studied control loop behaviors are examined experimentally in Petri net models. In addition, the function block options for bumpless transfer and setpoint tracking are also described. The proved configuration method for proper operations in balancing the interests of safety and availability is proposed.

157

Authors: Theagarajan Senthil, Balasubramanian Kannapiran

Energy conservation is the major concern of Wireless Sensor Networks, as the energy is the determining factor of the network lifetime. The reason is that the energy level of sensors has a great impact on the network lifetime. Several mechanisms have been proposed to minimize the energy consumption of nodes. In this paper, a cluster based, trustworthy topology management mechanism is presented and is proved to be energy efficient. The entire work relies on three important phases and they are cluster formation, chief node selection, trust degree computation and node’s state control. The cluster is formed by taking the location coordinates into account. This is followed by the selection of chief node. The chief node is responsible for computing the trust degree and to control the state of the constituent nodes. As limited count of nodes is involved in the process, the lifetime of the network is considerably improved. Finally, the proposed work is tested for its efficacy and the experimental results are satisfactory with respect to network lifetime.

175

Authors: Shyamala Loganathan, Renuka Devi Saravanan, Saswati Mukherjee

A cloud system uses virtualization technology to provide cloud resources (e.g. CPU, memory) to users in form of virtual machines. Job requests are assigned on these VMs for execution. Efficient job assignment on VMs will reduce the number of hosts used. Hence, it is essential to achieve energy optimization in cloud computing environments. Therefore, in this paper, a job scheduling mechanism is proposed to assign job to a VM of the existing active hosts itself by considering job classification and preemption. So that minimizing the number of host used in allocation intern reduces the energy consumption in the Cloud datacenter. In our proposed job scheduling algorithm, categorizing the job in to three different types and assigned based on preemption policy with the earliest available time of the resource (VM) which is attached to a host. Thereby, we reduce the energy consumption by making less number of hosts in the active state and increase the utilization of active host. Finally, we conduct simulations using CloudSim and compare our algorithm with other existing methods. Significant energy savings can be obtained depending on system loads. Energy saving is about 2% to 46% with respect to the non-energy aware algorithm, 1% to 7% than the energy aware algorithms.

193

Authors: Vangapelli Sowmya, Bulusu Vishnu Vardhan, Mantena S.V.S. Bhadri Raju

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two segments of text, even though the similar context is expressed using different words. The textual segments are word phrases, sentences, paragraphs or documents. The similarity can be measured using lexical, syntactic and semantic information embedded in the sentences. The STS task in SemEval workshop is viewed as a regression problem, where real-valued output is clipped to the range 0-5 on a sentence pair. In this paper, empirical evaluations are carried using lexical, syntactic and semantic features on STS 2016 dataset. A new syntactic feature, Phrase Entity Alignment (PEA) is proposed. A phrase entity is a conceptual unit in a sentence with a subject or an object and its describing words. PEA aligns phrase entities present in the sentences based on their similarity scores. STS score is measured by combing the similarity scores of all aligned phrase entities. The impact of PEA on semantic textual equivalence is depicted using Pearson correlation between system generated scores and the human annotations. The proposed system attains a mean score of 0.7454 using random forest regression model. The results indicate that the system using the lexical, syntactic and semantic features together with PEA feature perform comparably better than existing systems.

217

Authors: Samarth Borker, Sanjiv Bonde

This paper proposes a novel method for underwater image enhancement algorithm using Principal Component Analysis (PCA) fusion. For fusion process, the dual images are derived using single underwater degraded image. The novelty of this work is the fact that it does not demand any underwater scene parameters beforehand and is independent of prior based information. Secondly, we demonstrated the successful use of homomorphic filter and adaptive histogram equalization for individual color channels followed with image smoothing as two inputs to fusion scheme. The fused image is subsequently post-processed using color constancy technique for effective results. The visual and quantitative analysis of this method is carried out with contemporary underwater dehazing and enhancement algorithms. Metrics such as entropy and quantification of restored edges is utilized to validate our findings. The outcome reveals better exposure of dark areas, enhancement of edges, increase in overall contrast and preservation of natural colors.

235

Authors: Jay Prakash, Rakesh Kumar, Jai Prakash Saini

Mobile Ad-hoc Networks (MANETs) find numerous applications. Its utility can be extended by integrating it with Internet. Some real-life applications where such integrated networks can be used are disaster situation viz., earthquake, battle fields etc. MANETs are dynamic topology, infrastructure less and standalone networks of wireless mobile nodes. To overcome this limitation, MANET needs to connect to the Internet. In such networks, mobile nodes do not have wired connection, so they do not follow any particular route to transfer their packets. Thus, every time when topology changes, routing paths changes too. However, every node selects the shortest path to route their packet. In that case, this path will be congested due to overload. We propose a load balancing adaptive Internet gateway discovery approach which focuses on solving the problem of overload and congestion in mobile ad-hoc domain. Particle Swarm Optimization is used for selecting an optimal path among all paths available. Our work is to optimize this load balancing problem. We are using Particle Swarm Optimization (PSO) concept to overcome this problem. In our approach, we use soft computing technique viz., working procedure of PSO algorithm which searches the optimal paths using some mathematical function. Then, we combine shortest path algorithm with the gateway discovery algorithm. Thus, the algorithm first selects the shortest path stored in the memory and check whether it is free for routing packets or not, if the route is free or lightly loaded, then the source node will transfer packets otherwise if the node is overloaded, then PSO searches for the next optimal neighbour or node to route the packet. The new path is selected to route packets and stored in the place of current available path. Proposed algorithm has been implemented in MATLAB. Our approach outperforms the existing approaches.

256

Authors: Santhi Kannan, Saravanan Ramakrishnan

Cloud technology is broadly supported by healthcare organizations worldwide. Throughout the previous few years, healthcare industries have recognized the potential of cloud computing architecture and how it can assistance them to give quality services to patients. The cloud technology has faster the way healthcare industry can use or share information crossways a network. This paper offering an e-health solution founded on healthcare information systems joined with cloud computing concept emphasis on medical information processing. Our proposed work based on public cloud and request management application has been proposed and analysed in terms of waiting time defined as Quality of Service criteria (QoS). The service of the public cloud has been modelled using queueing theory as two serially connected queues (Tandem Queues) M/M/s and single server retrial queue (SSRQ). The results obtained in our proposed model are considerably reducing the total waiting time of different class of units in the cloud system than the existing system and more utilization of service in cloud.

274

Authors: Kavita Bhatnagar, Subhash Chander Gupta

Our previous article has proposed a simulation model to explore the nonlinear relationship between the nonlinear regime of the fiber, laser intensity and the error probability of the optical link. This paper extends the model to include the effective area of the fiber. In order to handle the extended parameter, time series feed forward neural network based prediction model is proposed. The reliability of the model is substantiated by extensive experimental analysis through which the relationship exhibiting between the laser intensity and the corresponding nonlinear regime and effective area of the fiber can be observed. The observed results demonstrate the performance of the prediction model in estimating the launch power to meet the desired error probability under varying nonlinear regime and effective of the fiber.

1

Authors: Vanaja Roselin Emmanvelraj Chirchi, Laxman Madhavrao Waghmare

In this paper, we propose a novel method for iris recognition system using enhanced isocentric segmentor (EISOS) and wavelet rectangular coder (WRC). At first, we locate the center of the eye within the area of the pupil on low resolution images using EISOS method. Once the iris region is successfully segmented, the next stage is to transform the iris region into the fixed dimensions. Then, we propose a novel feature vector generation method namely, wavelet rectangular coder (WRC). Finally, we recognize the iris image using fuzzy logic classifier to identify whether the iris image is present in the dataset or not. The iris recognition performance is measured using different dataset such as, CASIA, MMU and UBIRIS dataset. Experimental results indicate that the proposed method of EISOS+WRC based iris segmentation and recognition framework have outperformed by having better accuracy of 99.75% which is 94% and 93% for using existing approaches.

20

Authors: Loukal Keltoum, Benalia Leila, Bouguerra Abderrahmen

The mean of this paper is fuzzy sliding mode control of a Doubly Fed Induction Motor (DFIM); it’s the coupling of the fuzzy logic control and sliding mode control (SMC). The use of the sliding mode method provides very acceptable performance for DFIM control, and the chattering phenomenon effect is also eliminated by the fuzzy logic mode. In the first part, we carried out briefly a study of modeling on the full system. This model is intended to facilitate the procedures for setting and controlling the speed. We introduced the parameter variation to test the robustness of the control laws. The results of our simulations are conducted to validate the theory and indicate that the control performance of the DFIM is satisfactory and the proposed fuzzy sliding mode control (FSMC) can achieve favorable tracking performance.

38

Authors: Rama RaoAdimalla, Vatsavayi Valli Kumari, Ch Satyananda Reddy

Mobile ad-hoc network face a serious issue of frequent change of network topology which leads to the stability and reliability problems of routing. Many routing schemes such as multi-path routing and backup path routing are developed to increase the link reliability in the literature. Accordingly, we propose to design a backup route establishment for QoS routing protocol. In this protocol, the potential failures of network and node are identified and a backup routing is initiated. For detection of failures, a path evaluation function is determined based on the metrics energy drain rate and interference, congestion status are measured. The primary path satisfying the QoS parameters node’s static resource capacity, dynamic resource availability, neighborhood quality, link quality is established. Once failure is detected, back up routes are established and transmission is redirected on these back up routes. Simulation results show that the proposed protocol has reduced recovery delay and improved throughput as compared with the existing protocol. The improvement of 68% in terms of delay, 30% in terms of delivery ratio and 83% in terms of throughput are achieved for the proposed routing protocol.

58

Authors: Banoth Rajkumar, Gugulothu Narsimha

This paper proposes an incorporation mechanism of primitives to provide complete cryptography services for Mobile Ad-Hoc Networks (MANETs). The proposed approach is the Secure Light Weight Encryption Protocol for MANET, where an algorithm for providing availability with DoS resilience has been implemented to avoid TCP SYN flooding packets in the network and passes other packets and also an authentication code and hash function is generated. Later the intermediate nodes are allowed to recode the encoded data in the network. Through which the confidentiality and non-repudiation, key management, integration is achieved.

75

Authors: Thangavel Muthamilselvan, Balamurugan Balusamy

The growth of IT industry technology is absorbed by the cloud service technology, which leads to secure connectivity and availability of services to cloud users. This paper proposes an e-health data classification system that gives the services to e-health cloud user for their disease prediction requirements. The tree bagging method is suitable to select better weighted attributes and the careful seeding of K-means ++ algorithm improves the accuracy and speed of clustering. Most of the tree classification methods use only information gain as the strategy to select suitable attributes for classification. We used information gain in bagging technique to improve accuracy. In this research article, we proposed a method to Coalescing Decision tree classification algorithm, bagging technique, and K-means++ algorithm to build a better classifier for the e-health cloud users. It was evaluated with the standard data sets such as Diabetes, breast cancer, liver disorders and cardiotocography.

94

Authors: M. B. Jayalekshmi, S. H. Krishnaveni

Cloud computing is a paradigm which offers a range of services to the cloud users. Cloud storage is the most popular service, as the data owners are freed up from the data management and storage overhead. However, the data owners are concerned about the security of the data. In order to address this issue, this article presents a twin layered security scheme, which guarantees the security of the data and thus the data integrity is preserved. The proposed work involves two building blocks, which are hashing and encryption. The input data is separated into several blocks, followed by which a tree is constructed. This is followed by the computation of hash codes, which is done by the keccak-256 hash function. The hash codes are then encrypted by Advanced Encryption Standard (AES) (256) with a dynamic key being generated by CodeIgniter. Thus, the security of the system is tightened and misuse of the data is strictly denied. The performance of the proposed work is tested with respect to throughput, memory and time consumption. The experimental results are reasonable, when compared to the analogous algorithms.

117

Authors: Vyacheslav Shulunov, Irina Esheeva

The article considering the problem of quick transformation of solids of revolution into raster layer for Roll Powder Sintering (RPS) additive manufacturing technology. The author describes the algorithm for solving this problem. This work is devoted to the development and the validation of the impetuous algorithm for converting the 3D coordinates of a solid of revolution to spiral coordinates. It is based on the assumption that one point on the figure of revolution is predetermined multitude of points in the spiral coordinate system and these can be defined without calculations because of symmetry properties. Such method provides to accelerate the performance of the coordinate conversion thousand fold and real time forming objects 1.5 m3 in volume with layer thickness ~25 µm by non-powerful computers per hour. The proposed algorithm has been extensively tested and proven by numerical examples of several parts of computer aided design models. It is faster than other coordinates transformations and can be extended to the acceleration converting and processing geometry of 3D objects.

136

Authors: Pooja Choudhary, Jaisankar Natarajan

It is always an effective option for storing the data on cloud as it has advantages such as processing huge volume of data on user’s request. But policy updating is one of challenging issue when attribute based encryption method is used for constructing schemes such as access control. Many techniques have been proposed for achieving secure data access control in any cloud storage system, but policy updating is always a problem. In this paper, a novel scheme is proposed to enable to access control by using method known as dynamic policy updating for the data in cloud. The focus of this paper is to develop dynamic policy updating method for the data which is stored in the cloud. This method will help the data owners to update the policy for the data which is stored in the cloud. The features of this method are that data owner can update the policy on his data and data will be encrypted according to the new policy and user will be able to download only if he has new policy which is not possible in existing methods. The drawback of traditional method was removed such as user revocation and method is made more efficient access control scheme policy update based on attribute. The results show that policy can be updated for the data stored in the cloud and revoked user cannot download the file. Only the data owner can update the policy for his data in the cloud other than data owner cannot do this.

155

Authors: Jegan Govindasamy, Samundiswary Punniakodi

Nowadays, ZigBee is one of the dominating standards for wireless sensor networks and Internet of Things (IoT) networks. Even though, the ZigBee standard is formed with low per-unit costs, security in mind and network resilience, existing security mechanisms are not effective to provide security and protection against wormhole attacks and Distributed Denial of Service ( DDoS) attacks on networks such as WSN and IoT. They also introduce high consumption of energy, storage memory and processing. In this work, Energy Efficient Intrusion Detection System (EE-IDS) and Energy Efficient Intrusion Detection System with Energy Prediction (EE-IDSEP) are proposed for protection of ZigBee based wireless sensor networks in presence of wormhole attacks and ( DDoS) attacks. The EE-IDS is developed and its performance is evaluated by considering three different routing protocols such as Ad hoc On-Demand Distance Vector (AODV), Shortcut Tree Routing (STR) and Opportunistic Shortcut Tree Routing (OSTR) to improve the security against wormhole attack and to mitigate the energy consumption of the sensor nodes in the ZigBee based wireless sensor networks. The proposed EE-IDS and EE-IDSEP are evaluated through extensive simulations by using NS2 and then compared with the existing Energy Efficient Trust System for Wormhole detection (EE-TSW) and Energy Efficient Trust System (EE-TS) for detection of DDoS attack. It is inferred from the simulation results that proposed IDS namely EE-IDS-AODV, EE-IDS-STR and EE-IDS-OSTR for detection of wormhole attack have better performance than that of existing EE-TSW and proposed system EE-IDSEP for detection of DDoS attack have also shown better performance than that of existing system EE-TS in terms of performance metrics such as Packet Delivery Ratio (PDR), Average End-to-End Delay, energy consumption, detection rate, average detection time and False Positive Rate (FPR).

173

Authors: Malay Bhatt, Tejas Patalia

A large number of folk dance videos are uploaded on the web or added as a situational song in the Bollywood movies. The classification of folk dance videos is essential for dance education, to preserve cultural heritage, and for music companies to provide better customer oriented service. India is a country having many regional languages and each region has its own popular folk dances. Four different Indian folk dances namely, 'Garba', 'Lavani', 'Ghoomar' and 'Bhangra' are considered. A Folk Dance Classification Framework is proposed which extracts audio signal from video, takes a fragment of 125 seconds from the beginning and further separates it into a set of small segments, calculates Mel-frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients, generates high dimensional feature vector, reduces dimensionality using Principle Component Analysis (PCA) and classifies segments using Scale Conjugate Gradient Neural Network. The performances of chosen classifiers, K-Nearest Neighbor, Naïve Bayes and Neural Networks, are compared. Class labels of all segments are clubbed together and based on majority voting class label is assigned to a folk dance song. System achieves more than 90% accuracy.

192

Authors: Ghetiya Rutvi Babulal, Gayathri Prakasam

Proficient Algorithms for Mining High Utility Itemset in One Phase utilizing Direct Discovery of High Utility Patterns refers to the disclosure of object/item sets with high utility. The situation may end up being more terrible when the database contains long high utility itemsets.The information of high utility itemsets is kept up in a tree-based data structure UP-Tree to such a degree, to the point that required itemsets can be created productivity with only two yields of database. The execution of existing algorithm issue all utilize a two-stage, applicant era approach with an exemption which is however neither efficient nor versatile in case of huge databases. This two-stage approach experiences adaptability issue because of the enormous number of matching itemsets. This paper proposes a novel calculation that finds high utility examples in a solitary stage without creating candidates. Our straight information structure empowers us to process a tight technique for pruning and to specifically distinguish high utility examples in an efficient and versatile way, which focuses on the main driver with earlier technique. Test comes to conclusion that the proposed computations, especially Direct Discovery of High Utility Pattern reduce the amount of hopefuls satisfactorily and additionally beat diverse figuring extensively to the extent runtime, especially when databases contain clusters of long trades.

210

Authors: Thendral Puyalnithi, Madhuviswanatham Vankadara

Most of the existing Clinical Decision Support Systems(CDSS) for predicting Coronary Heart Disease(CHD) risks rely solely on the analysis of single type of diagnostic data or a certain type of classification algorithm, thus making the predicted component unreliable. Since the Risk value very much influences the further course of the treatment, the correctness of the risk value is eminent. So the novelty of the proposed method lies in considering many classification algorithms and many diagnostics data sets to predict the risk value. Moreover in the proposed method we have given flowchart for finding the risk value, which is a novelty. The results shows that the selection of classification algorithms cannot be universal as proposed by many recent researches and each type of data set will be having a certain type of classifier which works best for the data set. The proposed system has four classifiers namely Mixed cardio data classifier, Single Photon Emission Computed Tomography(SPECT) data classifier, Electrocardiogram(ECG) data classifier and 1-year survival Electrocardiogram data classifier, each trained with benchmark datasets of University of California, Irvine(UCI) Machine learning repository. The performance of algorithms such as Iterative Dichotomiser3(ID3), Naïve Bayes(NB), Support Vector Machine(SVM), k-Nearest Neighbors(kNN) and Ensemble classifiers such as Bagging and Boosting are evaluated for every data set and best classifier is chosen by its accuracy measure. Ensemble method is the most efficient for 1-year survival ECG data classifier with 89.5% accuracy, ID3 is the most efficient algorithm for Mixed cardiac data classifier with 96.9% accuracy and SVM is the most efficient algorithm for SPECT data classifier and ECG data Classifier with accuracy of 98.3% and 100% respectively. The proposed work also finds the optimal number of samples needed for Bagging and Boosting type of ensemble classifiers.

226

Authors: Leo Dencelin Xavier, Ramkumar Thirunavukarasu

Knowledge of a protein’s secondary structure, in turn, contributes to our understanding of the functions of the protein is vital to many aspects of living organisms such as those of enzymes, hormones, and structural material, etc. It also helps in designing new drugs for critical disease. In this paper, we have advocated a distributed approach to identify the Protein Secondary Structures using an ensemble method on protein primary sequences. The Ensemble based Random Forest algorithm has been adopted to build the three-way predictive model. Based on the amino acid features of each protein and decision tree parameters, the classification model allows us to assign protein structures as ‘α helix’, ‘β sheet’, or a coil. Also the proposed model is implemented in a distributed computing environment, SPARK. Experiments have been carried out using cross-validation tests on RS126 and CB513 benchmark datasets. Our results clearly confirm that ensemble approach in classifying protein secondary structures scores better accuracy with improved performance when it will be implemented in the distributed environment.

245

Authors: Alexis Cordovés García, Ricardo Sors Lancho, Arlys M. Lastre Aleaga, Alexis Cordovés Rodríguez, Leonardo L. Lorente Leyva

The goal of this paper is the adaptation of plans of manufacturing processes of Axisymmetric Solid Parts under the approach of Case Based Reasoning (CBR). To this end, a data model for feature-based representation has been developed, which contains the geometric, dimensional and technological information required for the preparation of the one-piece process plan. As a result, an algorithm to generate new parts process plans from a similar plan retrieved from the case base is presented, which is based on the specific information of the Primitive Form Features (PFF) and considers the possible interaction between neighboring PFFs. The computer support of the developed algorithm was implemented in AutoLISP as programming language of the CAD system used and the interface was created in OpenDCL.

257

Authors: Myungsuk Lee, Eunsook Bae

The purpose of this study is to explore a new teaching and learning method using MOOC as a way of solving the change of the educational environment and the learner 's unsatisfied academic desire. This study suggests 'Hybrid teaching method' as a way to utilize MOOC in liberal arts education. This teaching method is a method of perfect learning through formal learning and informal learning. Formal learning consisted of a teacher - centered knowledge acquisition stage and a learner - centered thinking extension stage. Informal learning consists of learner - led experience/reflection stages. It is a characteristic of 'Hybrid teaching method' that emphasizes the role of instructor, breaks the boundaries of education space through informal learning, and implements perfect learning through level learning. This study is meaningful that the MOOC has provided a detailed plan to utilize it in the curriculum of the university. We hope that the ‘Hybrid teaching method’ using MOOCs will be a way to adapt and lead the liberal education to the future education trends as a customized learning according to the development of science and technology, destruction of spatial areas such as online and offline, emphasis on connection between theory and reality through formal learning and informal learning.

272

Authors: Fatiha Bessaha, Nouria Mahrez, Souhila Bendenia, Fatima Kasmi, Kheira Marouf-Khelifa, Amine Khelifa

Algerian halloysite was heated at 600 °C and treated with HCl 5N. The materials were characterized by chemical analysis, TEM, and FT-IR spectroscopy, and used in the elimination of Congo red (CR) from aqueous solution. The effects of contact time and temperature were investigated. The thermal treatment at 600 °C results in the formation of dehydroxylated structure. Acid attack involves an increase in SiO2 content, due to the leaching of Al ions from octahedral sheet. Thermo-chemical treatment also diminishes the percentage of impurities and maintains the tubular morphology. Kinetic data follow the pseudo-second order model, whilst thermodynamic parameters lead to a not spontaneous and endothermic process. Significant changes occur in the vibrational spectrum of H600-5N (halloysite treated at 600 °C and with HCl 5N), after adsorption of Congo red, with the involvement of amino and sulfoxide groups. The mechanism highlights an outer-sphere surface complexation of SiOH....H2O species, i.e., SiOH linked to H2O via H-bonds.

290

Authors: Asha Jerlin, Jayakumar Chinnappan

Detection of malware has become more challenging today because of the advancements and technologies adapted to corrupt the network or the devices. Static, dynamic and hybrid malware detection analysis methods have failed to provide complete malware detection. Hence in this work, a bio inspired sequence alignment method used in bioinformatics to compare the similarity between amino acid sequences in protein structures has been adapted to give the best similarity score to detect malwares. The state of art sequence alignment methods like Smith Water Man Algorithm (SWMA) used in bio informatics suffers from the problem of more memory utilization and computation time which is in the order of n2 ie., (O(n2)) and hence in this work an efficient sequence alignment algorithm (ESSA) has been proposed to address the problem of memory utilization thereby making the memory utilization and computation time to the order of n ie., (O(n)) there by making the detection rate higher. It is also clear from the results that the similarity score is high when the sequence length is small. The accuracy of the prediction rate of malware and benign increases.

307

Authors: Gunasekaran Ganesan, Venkatesan MeenakshiSundram

3D visualization is the recent research need to be able to view the model from various angles so as to see data that is obscured by other data. While visualizing the data, view point selection is one of the major challenge which define the viewpoint information to select best viewpoints. This paper introduces teaching learning opacity based optimization (TLOO) and Kalman filter for selecting optimal viewpoint. The structural features of 3D volume dataset such as opacity and luminance is extracted for selecting optimal viewpoint position from that structural features. TLOO optimization algorithm identifies the voxels and standard information using view point evolution function. Then, kalman filter steers the optimization process with the help of cost function. The proposed flow of the technique is simple and very effective for selecting optimal viewpoints in 3D images. Computational complexity and the quality metrics are analyzed that provided maximum output efficiency.

327

Authors: Nagaraja Gundluru, Pradeep Reddy Ch

We can realize instant joint group communication by forming Mobile Ad Hoc Networks without demanding any pre-plan or pre-existing infrastructure setup. Conversely, the curbs of these networks such as unreliable wireless medium, unpredictable topology, no central administration, fuel the compulsion of a key based cryptographic algorithm to defend data traffic. In this perspective, substantial research work has been done in the last decade or so and ascertained that the trust based frameworks for group key management deliver superior performance than others. Since the nodes in ad hoc networks have limited computing resources, the overall performance of the system depends on how effectively and securely designed the system. This encourages us to work on a framework which consumes less computing power and also invulnerable to internal as well as external attacks. We propose a framework which reduces the network resource consumption for the trust request and collection using game theory concept. The energy of the wireless nodes are significantly saved by choosing the novel strategy called as finding optimal set of remote nodes to send the response for trust request using game theory. Choosing local optimal at each stage eventually leads to global optimal. Later, synthesize the collected trust and handle the attacks using fuzzy concept in order to get the degree of trustworthiness instead of binary classification. We prove with our simulation results that our proposed scheme reduces overhead of the network significantly while without compromising security aspects.

347

Authors: Balamanigandan Ramachandran, Kamalraj Subramaniam

During the last decade internet users, researchers and industries have focused on the cloud based technologies. Cloud technology is a distributed and scalable service model and in this model service providers offer the services depending on the cloud users. Security in cloud is a challenge for the service providers , data owners and the service requesters because the cloud providers provide various services such as storage, computing , database and network . All the services focus on data processing in a network to share the information from cloud to users, data owner to cloud and cloud to authenticator. To solve these security issues, in this paper we define a new approach for data sharing issues and service management of Trusted Third Party Authentication ( TPA ) based on multilevel security framework and bilinear pairing techniques. This framework consists of three modules and these modules are service module, transaction processing module and security module . The next thing is bilinear pairing technique, which uses the pairing techniques to achieve the secure cloud service and improve the computing utility power , store large amount of data and execute the efficient encryption process securely in cloud environment. In this work, the cloud ssecurity requirement has been enhanced using bilinear mapping and multilevel security framework . The result of multilevel security framework is comparatively better than the present day cloud security.

363

Authors: Kumaran Umapathy, Neelu Khare

Preservation of privacy is a significant aspect of data mining and as the secrecy of sensitive information must be maintained while sharing the data among different untrusted parties. There are many application is suffering from vulnerable, data leakage, data misuse, and sensitive data disclosure issues. To protect the privacy of sensitive data without losing the usability of data, various techniques have been used in privacy-preserving data mining (PPDM). Some of the approaches are available to maintain the tight privacy, but they fail to minimize the execution time and error rate. The main objective of the article is to contribute and retrieve the data with minimal classification error and execution time with enhanced privacy. To overcome the issues, the paper introduces the Secure Data Contribution Retrieval algorithm (SDCRA) to fulfill the current issues. Proposed algorithms define a privacy policy and arrange the security based on requirements. This design applies the privacy based on the compatibility of applications. This approach is capable of satisfying the accuracy constraints for multiple datasets. It also considers the efficient data extraction with a good ranking of attributes in tables. Here, proposed SDCRA is compared with existing approaches namely as Perturbation, singular value decomposition (SVD), Singular Value Decomposition data Perturbation (SVD+DP), K-anonymity with Decision Tree (KA+DT)[] for Cancer, HIV, Diabetes dataset. Based on experimental result proposed approach performs well regarding success rate, error rate and system execution time compare than existing methods. Proposed approach improves Success Rate 1.83% reduces the Error Rate 2.33% and minimizes the system execution time 2 seconds.

381

Authors: Shachi Mall, Umesh Chandra Jaiswal

This paper developed innovative algorithms such as shallow parsing and modified Lesk’s algorithm to resolve the issues in Word Sense Disambiguation and performed correct translation from Hindi language to English language. Shallow parsing method is based on Hidden Markov model. We also perform an evaluation for 1657 Hindi tokens with 990 phrases for Parts of speech tagging and Chunking for given Hindi sentence as input and able to achieve Precision, Recall, F-score, Accuracy for Parts of speech tagger: Accuracy: 92.09%; precision: 84.76%; recall: 89.29%; F-score: 86.97, system accuracy for Chunk: Accuracy: 93.96%; precision: 89.33%; recall: 91.31%; F-score: 90.315%. The evaluation is performed by developing confusion matrix in which the system result of Parts of speech tagger and Chunk is compared with Gold standard date provided by IIIT Hyderabad in the summer school 2015. In this paper we discuss the second problem Word Sense Disambiguation in which we enhance the Modified Lesk algorithms by using overlap based method which will find information between three pieces of words in a given context. The system generated result resolves the issues of Word Sense Disambiguation and shows the comparison result with the website Google Translator in which we input polysemy word in a Hindi sentence and same sentence input in our generated system and shows the comparison result of both. The output result shows that our system resolves Word Sense Disambiguation and produces correct translation and Google Translator is fails to resolves the correct Translation.

401

Authors: Gunavathi Chellamuthu, Premalatha Kandasamy, Sivasubramanian Kanagaraj

Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. The classification of cancer is a foremost area of research in the field of bioinformatics. Microarray technology enables the researcher to investigate the expression levels of thousands of genes in a single experiment and gives the measurements of their differential expression. The main drawback of gene expression data is that it contains thousands of genes and a very few samples. Feature or gene selection methods are used to successfully extract the genes that directly involved in the classification and to eliminate irrelevant genes. These methods considerably improve the classification accuracy. The proposed method applies bat algorithm (BA) for feature selection in tumour classification. Initially, the top-10 genes are selected by T-Statistics, signal-to-noise ratio (SNR) and F-Test. The classifier accuracy of k-nearest neighbour (kNN) technique is used as the fitness function for BA. The simulated results are demonstrated and analyzed with 10 different cancer gene expression dataset. For Lung Cancer Michigan and Lung Harvard2 datasets the proposed method gives 100% classification accuracy with minimum number of genes. For 5 other datasets, the proposed method gives more than 90% of classification accuracy. The results show the suitability of the proposed algorithm for feature selection in cancer classification.

418

Authors: Haritha Akkineni, Venkata Swarajya Lakshmi Papineni, Vijaya Babu Burra

Ubiquitous online communication is producing massive amounts of data on an un-precedential scale. Many have come up in analyzing the data produced from Twitter. Sentiment analysis is being carried out to catch the pulse of the people. As the data is mostly in the unstructured format. Getting the summary of what has been expressed positively and what has been expressed negatively plays a major role. Summaries take a wide strand in this plait as the relevant content can't be comprehended all at once. Working on the premise that online social media conversations might represent a new source of information to monitor the status of the policies launched by the government, this investigate stands as a first paw which may ultimately be the source for producing abstractive summaries from Twitter. This paper mainly focuses on developing a hybrid method which takes the combination of extractive summaries using statistical approaches as well as abstractive summaries using Rich Semantic Graph based approaches. The set of classified positive and the negative tweets will be passed as input to the hybrid method and it generates concise as well as readable summaries from both set of tweets. These summaries can be easily viewable on the PDA's and enhances the decision making capability of the policymaker.

435

Authors: Dhivya Elavarasan, Durai Raj Vincent

Web mining is an application of data mining techniques to extract and process knowledge from sources such as web documents, hyperlinks, website usage logs etc. Due to the outbreak of extensive information through web resources, mining of semantically relevant data for a search keyword is one of the most intriguing studies to research. This work deals with automatically extracting the information from web documents using web content mining. The extracted data needs to be preprocessed in order to obtain the appropriate data format for further analysis. Generally, the content based search algorithm is used to find the items relevant to the keyword searched resulting in an indexed set of similar results. Further, the clustering of the similar data is done by adopting the quality threshold clustering algorithm assigning a similarity index to each of the result items. For the final list of items obtained, the weighted page ranking algorithm is applied to rank the most frequently searched item in the lists. The proposed work efficiency will be determined by the cluster's quality and the query blocks ranking efficiency. Various metrics like cluster purity, NMI, Rand Index, F-Measure, wPRF are used to evaluate the quality and the ranking efficiency of the search result obtained. Duplicate result sets are handled and are castigated for better unambiguous results. The results obtained is proved to be better and to overrun the existing approaches like QFI and QFJ. The quality of the result set obtained is further evaluated by repeating the process considering only the top n ranked items, shuffling the top items or by randomly selecting the items. Thus, enabling to validate and uphold the results of the proposed work surpassing the existing algorithms.

1

Authors: Tamilarasan Santhamurthy, Kumar Parasuramane

Today’s wireless communication systems required groundbreaking solution to meet scarcity of radio spectrum. Cognitive radio (CR) is an emerging technology which provides futuristic solution and improving the precious likely resources utilization. Cognitive radio network (CRN) is an intellectual wireless communication system as well as aware of its environment. In CRN, we have sensing problem by multiple nodes with changing computational power, spectrum allocation and range of sensing. The secondary user (SU) plays major role of primary channel sensing and appropriate unused channel selection for communication. In this paper we focused on optimum resource allocations and priority based scheduling for primary user (PU) and secondary user (SU) to exploit the performance of the entire network by reducing the interference at PU and also exploiting the throughput and decreasing the risk of overlying the coverage of CRNs. The effectiveness of dynamic resource allocation priority based scheduling (DRAPBS-CRN) algorithm is compared with dynamic resource allocation (DRA-CRN) algorithm and is found to be 26% more efficient using ns-2 simulation. This proposed DRAPBS-CRN algorithm also will avoid collisions and perform smart data transmission in CRN.

21

Authors: M.N.Shah Zainudin, Md Nasir Sulaiman, Norwati Mustapha, Thinagaran Perumal, Azree Shahrel Ahmad Nazri, Raihani Mohamed, Syaifulnizam Abd Manaf

In various classification areas, the curse of dimensionality becomes a major challenge among the researchers. Thus, feature selection plays an important role in overcoming dimensionality problem. Relief-f is one of the filter methods to rank the most significant features based on their relevance. Although relief-f proved to be a powerful technique in filter strategy, but this method only rank the features based on their significant level. Hence, feature selection is embedded to select the most meaningful features based on their rank. Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. Simple and powerful in implementation, we combined relief-f with DE in our proposed feature selection method to solving the optimization problem. In this work, population size and generation size were adaptively determined from the number of features from relief-f. The performance of proposed method is compared with several feature selection techniques in order to prove their superiority using ten datasets obtained from UCI machine learning repository.

40

Authors: Anitha Arumalla, Madhavi Latha Makkena

Reconfigurable architectures have provided a low cost, fast turnaround platform for the development and deployment of designs in communication and signal processing applications. The floating point operations are used in most of the signal processing applications that require high precision and good accuracy. In this paper, an effective implementation of Fused Floating-point Add-Subtract (FFAS) unit with a modification in dual path design is presented. To enhance the performance of FFAS unit for reconfigurable architectures, a dual path unit with a modification in close path design is proposed. The proposed design is targeted on a Xilinx Virtex-6 device and implemented on ML605 Evaluation board for single, double and double extended precision. When compared to discrete floating point adder design, the FFAS unit reduces area requirement and power dissipation as the later shares common logic. A Dual Path FFAS (DPFFAS) unit has reduced latency when compared with FFAS unit. The latency is further reduced with the proposed modified DPFFAS when compared with DPFFAS for reconfigurable architectures.

58

Authors: Ramani Selvanambi, Jaisankar Natarajan

In data mining, association rules are produced in view of solid relations and regularities existing among the variables in extensive exchanges. These association rules go for extricating connections, frequent patterns and associations among the item sets in exchanges. Association rules are connected for use in different zones, for example, media transmission, hazard administration and so forth. One such territory where the association rules are vital is the stock market. In stock marketing, picking the right stock relies on upon the genuine stock quality and the capacity to pick the stock is urgent as it impacts the profit of investors. In this work, the proposed technique develops a novel and effective way to deal with producing optimal stock rules to help in the stock market prediction by utilizing Enhanced Apriori algorithm and Artificial Bee Colony Optimization (ABC) algorithm. In the result profit of PP, EMA, ROC and RSI for min-support 3, 4, 5 and 6 individually in rule mining for HCL stock market dataset is appeared with correlation of GA-Apriori algorithm, AGA- Enhanced Apriori algorithm and ABC- Enhanced Apriori algorithm. From this Artificial Bee Colony (ABC) optimization algorithm is performed when contrasting and different strategies and grant that strong association rules to be produced.

78

Authors: Palamakula Ramesh Babu, Parimala Venkata Krishna

The MAC layer plays a major role in the enhancement of the performance of any network. Hence this paper proposes a modified virtual backoff algorithm which queues the packets based on the delay that application can tolerate and ant colony optimization technique is used to choose the best of all available forwarding nodes. Two counter referred as Naccess and Nattempt are maintained for each node which is used to calculate the success rate of each node based on which the node is given the channel access of all contending nodes. Sleep scheduling is incorporated in order to enhance the energy conservation and hence increase the lifetime of the network. The performance of the proposed algorithm is proved to be better when compared to the legacy systems like VBA, S-MAC and EACAODV. The parameters used for testing are packet delivery ratio, average energy consumption, delay and number of collisions.

96

Authors: Sunitha Badanahatti, Yelisetty Satya Sree Rama Murthy

Regression testing is a kind of software testing that authenticates that software previously developed and established still accomplishes correctly after it was altered or interfaced with other software. The aim of the investigation is to progress the software competence via cloud based regression testing. The projected technique has three chief stages such as, 1) test case generation, 2) clustering and 3) test case prioritization. Primarily the input implementation is send to the cloud for test case generation. For producing the test cases the projected method utilizes coverage metrics. Following the test case generation, the existing test cases are gathered with the help of kernel fuzzy c means clustering algorithm. Subsequently each group is fed to test case prioritization; in that grey wolf optimization algorithm is utilized for test case prioritization. Thus we will get operative prioritized test cases. Our method will be implemented on JAVA with Cloud Sim platform. The presentation will be assessed using execution time and memory use. From the solution the applied method precedes minimum implementation time associated with the available technique.

116

Authors: Nageswara Rao Sirisala, C. Shoba Bindu

Evaluation of node’s trust value is certainly advantage in mobile Adhoc networks (MANETs) where the applications run efficiently by involving trustable nodes only. The proposed method “A Novel QoS Trust computation in MANETs using Fuzzy Petri Nets-QTFPN”, evaluates node trust value based on its quality of service (QoS) parameters. Here the MANET is represented as Dynamic Adaptive Fuzzy Petri Nets (DAFPN) model with concurrent reasoning algorithm (CRA). In which delivery of each packet from node to node requires evaluation of certainty factor (μ) using fuzzy expert system. This fuzzy inference system uses QoS parameters as fuzzy input variables namely energy, bandwidth, node mobility and reliability. In the routing process the intermediate node’s trust values are evaluated based on certainty factor. The concurrent reasoning algorithm can strengthen the proposed method in selection of quality path to destination and reestablishment of path in case of path breaks. The proposed method performance is analyzed theoretically in terms of time and space complexities. The simulation results are taken against node velocity and network size, where the proposed method outperforms the existing protocols.

136

Authors: Anggun Fitrian Isnawati, Risanuri Hidayat, Selo Sulistyo, I Wayan Mustika

Dynamic user in mobile communication encourages the implementation of self-organized and non-cooperative distributed power control. To be implemented, the power control must meet the feasible and convergent conditions. Feasibility of power control must be qualified for non-negative power vectors and the limit of maximum power, whereas convergence is tested by the speed for achieving of convergent conditions on fixed point. If it is feasible, then the system will be convergent, but when it is infeasible then the power transmit of user will be negative and the system is never reach the convergence. While the semi feasible condition requires the implementation of a proposed method of HDCPC. When power transmit of user exceeded the maximum power, Pmax, HDCPC method choose the transmit power that equal with ½ Pmax compared to use Pmax that required on DCPC or to force the user turned off the power transmit on GDCPC. Results showed that it would be more efficient in power usage than DCPC and more implementable than GDCPC. Proposed HDCPC is done when there is no option of handover channel, while the results after user move to another channel is able to achieve the SINR target and spent less power. Related to the convergence analysis, it can be concluded that the larger the SINR target, the longer iterations required to achieve a convergent condition.

156

Authors: Harshita Patel, Ghanshyam Singh Thakur

Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted nearest neighbor method that optimally handle the imbalance issue of data. Use of optimal weights improve the performance of fuzzy nearest neighbor algorithm for default balanced distribution of data, for the classification of imbalanced data concept of adaptive K is merged with it that apply large K, number of nearest neighbors for large class and small K for small class. We deploy this combination to classify imbalanced data with better accuracy for different evaluation measures. Experimental results affirm that our proposed method perform well than the traditional fuzzy nearest neighbor classification for these type of data sets.

173

Authors: Achyut Shankar1*, Natarajan Jaisankar

In this paper, we propose an (SAPSO) Self-Adaptive Particle Swarm Optimization algorithm to solve the base station positioning problem. This algorithm is used to minimize the distance between the base station and cluster head owing to maximizing the energy. Moreover, the proposed algorithm is compared with many other conventional algorithms such as Artificial Bee Colony-Dynamic Scout bee (ABC-DS) algorithm, Genetic algorithm (GA), Particle swarm optimization (PSO) and Firefly with Dual Update Process (FFDUP) algorithm. Also, empowering SAPSO helps to find the optimal location of the base station to conserve energy in a noteworthy manner to maximize the network lifetime and prolong network connectivity regarding minimizing distance, delay and maximizing the security, energy. Our simulation results demonstrate that the proposed SAPSO algorithm performs better than the other conventional algorithm.

1

Authors: Lahcen Mountassir, Touriya Bassidi, Salma Aziam, Hassan Nounah

This article intends to present a time-frequency representations adapted to the characterization of steels damaged by the environment. These techniques are applied for analyzing experimental acoustic signal backscattered by corroded steel immersed in water. These signals are processed using a spectrogram analysis, Pseudo Wigner-Ville Distribution PWV and continuous wavelet, whose interest is to provide a signal representation in time-frequency. The goal here is whether each pattern of representation is due to corrosion or default in the plate. The use of time-frequency representations for characterizing steels damaged by the environment proves interesting because of their ability to localize the energy of the signals in a two-dimensional plane. However, bearing in mind that these representations should check drastic properties about the studied signal, choose a representation adapted case by case. The results obtained highlight the advantage of using time-frequency representations for the study of the acoustic wave’s propagation in corroded steels.

22

Authors: Ramanjaneya Reddy Nalavala, Chenna Reddy Pakanati, Padmavathamma Mokkala

Multiple senders share bandwidth and send their data in high speed over the wired network. This may lead to congestion in the network. Congestion control plays a prominent role in network resource management. Effective mechanisms are needed to control the congestion in the network. In Transport layer, TCP Friendly Rate Control (TFRC) has been widely used for advanced multimedia streaming application over wired networks to give best smoothing sending rate. However, there exists packet loss due to high traffic in the network. To alleviate the barriers of congestion and reduce packet loss in the network, one needs to implement best queue management mechanisms to help the TFRC. In this paper, we have applied enhanced queue management scheme to TFRC. The simulation results have shown that this strategy reduces packet loss and increases throughput.

38

Authors: Balamurali Ananthanarayanan, Siva Balan, Anu Meera Balamurali, Karthika Balamurali

In the field of weather forecasting, especially in rainfall prediction many researchers employed different data mining techniques. There is numerous method of organizing agricultural engineering substance and it remains an open research issue particularly when taking to distinctive arrangements of clients - farmers, agricultural engineers, agri-organizations -both from proficiency point of view. Keeping these factors Indian farmers in mind, we have chosen to do research on efficient dissemination of rainfall forecasting to safeguard farmers from crop failure using optimized neural network (NN) model. Here, at first, we generate the feature matrix based on five feature indicator. Once the feature matrix is formed, the prediction is done based on the hybrid classifier. In hybrid classifier, particle swarm optimization algorithm is combined with Grey Wolf optimization for training the RBF NN. The performance of the algorithm is analyzed with the help of real datasets gathered from pechiparai and perunchani regions.

56

Authors: Patel Harshita, Thakur Ghanshyam Singh

Classification of imbalanced datasets is one of the widely explored challenges of the decade. The imbalance occurs in many real world datasets due to uneven distribution of data into classes, i.e. one class has more instances while others have a few that results in the biased performances of traditional classifiers towards the majority class with large number of instances and ignorance of other classes with less data. Many solutions have been proposed to deal with this issue in various crisp and fuzzy methods. This paper proposes a new hybrid fuzzy weighted nearest neighbor approach to find better overall classification performance for both minority and majority classes of imbalanced data. Benefits of neighbor weighted K nearest neighbor approach i.e. assignment of large weights to small classes and small weights to large classes are merged with fuzzy logic. Fuzzy classification helps in classifying objects more adequately as it determines that how much an object belongs to a class. Experimental results exhibit the improvements in classification of imbalanced data of different imbalance ratios in comparison with other methods.

75

Authors: M. LawanyaShri, S. Subha, Balamurugan Balusamy

An effective task scheduling is one of the vital aspects for effectually hitching the potential of cloud computing. The most important aspect of task scheduling focuses on balancing the load of tasks among virtual machines, which is independent in nature. Energy conservation is one of the major key issues in cloud environment which in turn reduces operation costs in cloud datacenter. Meanwhile, Energy-aware load balancing optimisation technique is a promising way to attain the goal. To ensure fast processing time and optimum utilization of the cloud resources, we propose an energy-aware Fruit fly optimisation algorithm (EFOA-LB) for balancing the load among virtual machines in the cloud system. The energy-aware EFOA-LB is a modern swarm intelligence algorithm inspired by the foraging behavior of fruit flies, aims to attain well-balanced load on virtual machines and reduces energy consumption accordingly. Based on results obtained from our simulations, the proposed algorithms minimizes makespan and reduces the energy consumption of the datacenter, while meeting the task performance. The experiment results indicate that the energy-aware EFOA-LB algorithm is more efficient than the existing load balancing algorithms.

95

Authors: Yarabham Gangadhar, Venkata Sesha Giridhar Akula, Chenna Reddy Pakanati

Watermarking is one of the efficient approaches for digital authentication. An adaptive feature point extraction model is proposed in this paper for robust watermarking. The host image is treated with number of geometric attacks for extracting the consistent feature points form the image. The proposed watermarking scheme follows the adaptive feature point extraction method to retrieve the feature points from the host image. The response value of the feature points is calculated for improving the selection of feature points. The watermark insertion procedure is employed by inserting the watermarking bits with in the place of feature points. The feature point portion is extracted from the image and replaces the portion with watermarking bits. The watermark extraction procedure is used to restore the original image from the watermarked image. The watermarking bits in the image are replaced by the feature point portion for restoring the original image. The simulation experiment is carried with the MATLAB simulator. The proposed algorithm is tested with geometric attacks such as scaling, rotation, noise pollution and JPEG compression. The proposed method proved its efficiency when compared to other remaining algorithms.

114

Authors: Ramanathan L, Angelina Geetha, Khalid M, Swarnalatha P

Early prediction of student performance helps to take action for better achievements of students. To achieve the better education standard, several attempts have been made to predict the performance of the student, but the prediction accuracy is not acceptable. To accomplish the enhanced prediction, neural network (NN) based method is proposed. In this paper, an approach to predict student academic performance in college education based on Lion-Wolf artificial neural network is proposed. Lion algorithm and Grey Wolf optimizer is integrated to develop a Lion-wolf training algorithm to find the optimal weight for every neuron in NN. The proposed prediction model is validated based on Mean Squared Error (MSE) & Root Mean Square Error (RMSE) with the existing NN based prediction model. The experimental results show that performance of proposed prediction model is improved compared to existing prediction model with MSE of 5.25 and RMSE of 2.3.

134

Authors: Shakkeera L, Latha Tamilselvan

With the rapid and tremendous growth of real-time resource discovery process ubiquitously, Internet of Things (IoT) and mobile cloud technologies have become an essential part of the future internet. The adaptive and energy-efficient mobile application execution is still in its infancy stage due to the diversity of IoT applications, manipulating workflow of an application and handling a large volume of streaming data in the heterogeneous resource-rich environment. To overcome these constraints, this paper introduces a GAming IoT workflow Fuzzifiers and Supporting MAximum Resource utilization and optimal Task execution on mobile cloud (GAF-SMART) paradigm offering an intelligent way of computing the mobile applications on the remote server. The GAF-SMART employs Game-theory and Fuzzy logic assisted adaptive task scheduler to attain optimal execution for maintaining a workflow of an application. Thus, the proposed mobile cloud IoT paradigm offers high Quality of Service (QoS), increases resource utilization and minimizes the mobile device energy.

154

Authors: Deepa Mani, Anand Mahendran

Cloud management organisation is an imperative part of cloud computing platform and serving as the resource manager for cloud platforms. The multifaceted nature of cloud-management base makes its high availability (HA), a standout amongst the most necessities. Different innovations have been produced to build a consistent quality and availability of cloud administration infrastructure. In any case, little work concentrated on quantitative examination of its accessibility. While this ability accomplishes a developed availability with small fault rates, corporate requests conveyed over the autonomous zones may encounter unique Quality of Service (QoS) because of various physical frameworks. The key target of this paper is to show how the Markov-based model can fulfil the client request. For this reason, a few scenarios of the failure rate of virtual machine's practices, for example, single system failure, multiple system failures, power outage are considered by applying the Markov model. The improved repair strategies of accessibility in various circumstances are also investigated. The Queuing models like Markovian and non-Markovian models are examined using phase type expansion and renewal theory keeping in mind the end goal to sufficiently speak to and to assess. The considered element unwavering quality perspectives if there should be an occurrence of the most part dispersed lifetimes and times to repair.

176

Authors: Ramani Selvanambi, Jaisankar Natarajan

The main aim of the proposed method is to remove cyclic repeated patterns in sequential pattern mining. Initially the input dataset is fed to the clustering process, in which fuzzy c means clustering algorithm is used to cluster the available data based on the similar sequential pattern. This approach is able to mine the patterns with the help of association rule mining, here two major tasks are present one is frequent item set generation and rule generation. In frequent item set generation, support and confidence value is evaluated for each pattern. Based on that, the rules are generated in rule generation. After the mining process, the threshold is fixed and based on that repeated cyclic patterns are removed and stored to the database. The performance of the suggested method is evaluated by means of execution time, memory and database difference ratio. The implementation is done with the help of JAVA platform.

6

Authors: Emansa Hasri Putra, Risanuri Hidayat, Widyawan, I Wayan Mustika

Routing process in Wireless Multimedia Sensor Networks (WMSNs) has its own challenges to pass multimedia applications from source nodes to a sink. The WMSNs have specific characteristics consisting of multi-hop network, restricted bandwidth, and limited power resources in the WMSNs. Additionally, multimedia applications require specific Quality of Service (QoS). This paper proposes a routing optimization based on the Ant Colony Optimization (ACO) for multimedia transmission in the WMSNs. The routing optimization can deliver multimedia data within an average throughput of 164.65 kbps and provide better QoS than DSDV and AODV routing protocols.

8

Authors: M. LawanyaShri, Balamurugan Balusamy, S. Subha

Cloud computing empowers sharing of computing resources dynamically to a wide range of end users. The workload on the cloud resources is amassed immensely with the advancement of new applications. To increase the utilisation of the virtual machine in the cloud data center, an efficient load balancing technique is predominantly crucial. Load balancing is the heart of the data centre in cloud environment which makes all the virtual machines accomplish the same amount of workload and helps the virtual machines deliver the services with minimal time delay. To ensure optimum resource usage and fast processing time, M/G/1 is developed with vacation queuing model (VQM-LB) for adjusting the workload of the under-loaded virtual machine in a cloud environment. The proposed system uses vacation and threshold policy to efficiently control the workload level of each virtual machine in the data center , and reduces the energy consumption and cost accordingly. The result obtained in our proposed model assuredly bounces optimum solutions and apparently indicates the triumph of an efficient load balancing of tasks on minimal energy consumption and cost.

9

Authors: Salem Mohammed, Khelfi Mohamed Fayçel, Bachir Bouiajra Rochdi

Harmony search (HS) is a new meta-heuristic optimization method inspired from improvisation process in jazz music searching for a perfect state of harmony. HS has been improved based on parameters settings updating and hybridization. In this paper, HS and four of its variants are compared via Wilcoxon and Friedman nonparametric tests using benchmark functions. Statistical analysis highlights the Global best harmony search as the best variant. Harmony Search algorithm and its variants are also applied to adjust the gains of PID controller of an inverted pendulum nonlinear system giving remarkable results when compared to genetic algorithm.

9

Authors: Rajesh Kaluri, Pradeep Reddy Ch

Sign language is the most communal language used by the deaf people among themselves for any kind of communication. People who are not familiar with Sign Language face difficulty in interacting with the deaf people. Hence, an effective system should be established to attain and discriminate the sign language. In our proposed system, we developed a framework for extracting and recognizing the sign gesture language and which is classified into four stages like noise removal using median filter, Segmentation using Modified Region Growing Algorithm (MRGA), feature extraction and recognition using Adaptive Genetic Fuzzy Classifier (AGFC). We have used Genetic algorithm with Fuzzy classifier to find out the optimal rules generated by Fuzzy classifier.

10

Authors: M. M. Gowthul Alam, S. Baulkani

Nowadays data growth is directly proportional to time and it is a major challenge to store the data in an organised fashion. Document clustering is the solution for organising relevant documents together. In this paper, a web clustering algorithm namely WDC-KABC is proposed to cluster the web documents effectively. The proposed algorithm uses the features of both K-means and Artificial Bee Colony (ABC) clustering algorithm. In this paper, ABC algorithm is employed as the global search optimizer and K-means is used for refining the solutions. Thus, the quality of the cluster is improved. The performance of WDC-KABC is analysed with four different datasets (webkb, wap, rec0 and 7sectors). The proposed algorithm is compared with existing algorithms such as K-means, Particle Swarm Optimization, Hybrid of Particle Swarm Optimization and K-means and Ant Colony Optimization. The experimental results of WDC-KABC are satisfactory, in terms of precision, recall, f-measure, accuracy and error rate.

10

Authors: M. Siva, R. Balamurugan, L. Lakshminarasimman

This paper presents the application of Water Wave Optimization Algorithm (WWOA) for solving economic dispatch problems including practical generator constraints. WWOA is inspired by the shallow water wave theory. The efficiency of the WWO Algorithm for solving economic dispatch problems is demonstrated by implementing it on three test systems having three, six and fifteen generating units with non-linear characteristics of the generator such as ramp-rate limits, prohibited operating zones including the system transmission losses. The results of the proposed approach have been compared with existing results obtained by other solution techniques. The test results reveal the capability of the proposed algorithm as an effective tool for solving various economic dispatch problems in a power system.

10

Authors: Rainu Nandal, Rahul Rishi

The rapidly rising and widespread use of database technology - including heterogeneous, geo-referenced and multidimensional data - there is a growing interest in developing new techniques for extracting knowledge from data. These techniques are the subject of the emerging field of Knowledge Discovery in Databases (KDD). In this paper, an algorithm to mine the intelligence rules from the input data using gravitational search (GS) algorithm is proposed. Initially, the significant patterns are selected from the input database with the use of measures like density ratio, sequence index, and density index. Subsequently, R-tree is modelled from the reduced dataset and is solved using the recently developed optimization algorithm. The rules obtained from the GS algorithm are useful for knowledge discovery. The experimentation is carried using the datasets to evaluate the performance in terms of mined rules and the computation time. The Performance metrics employed are a number of optimized rules, computation time and memory usage. From the analysis, it can be seen that the number of optimized rules decrease with increase in threshold and support.

10

Authors: Sasikumar Karuppusamy, Madiajagan Muthaiyan

The IT systems sent to satisfy their business functionalities and to provide good Quality of Service (QoS) parameters such as availability, scalability and performance. The failures are normal rather than exceptional in case of cloud computing. To improve system availability enhancing the system performance is an important factor. For that replicating the popular data to multiple sites is a best choice as the users can access the data from a nearby site. In this paper, we proposed a dynamic replication and placement algorithm to enhance the performance of the software system. We calculate popularity degree and replica factor to identify the suitable file to replicate. From this the most accessed file and the file need to be replicated is found and the replicate of the file is created. Then the proposed algorithm is used to place the replicas in the suitable location. We compare the performance of the technique with the existing technique.

10

Authors: Srinivasan Gopalan, Murugappan Shanmugam S

Interference is the most important issue in Wireless Sensor Network (WSN) as it causes severe degradation during communication. Also, as sensor nodes are power constraint, hence the system is expected to operate for longer duration without any delay during communication. In order to overcome these issue a delay sensitive and interference aware MAC protocol for WSN is proposed. In this paper, first MAC protocol is proposed which divides network into layer and assign node to each layer to avoid data loss in the network. Once network is divided to the multi-layer, timeslot is assigned to each node. To utilize the channel efficiently, timeslot steal mechanism is implemented. To avoid interference, a hybrid prioritization scheme is implemented to prioritize the packet queue based on weight of the packet queue and based on the idle time of the prioritized packet.

10

Authors: Kuruva Lakshmanna, Neelu Khare

DNA Sequence mining helps in discovering the patterns which can occur frequently, structures of DNA in DNA data sets. Frequent pattern mining is a central strategy for affiliation guideline discovery, but existing calculations experience the ill effects of low effectiveness or poor error rate on the grounds that natural groupings vary from general successions with more attributes. In our last work, we proposed Prefix Span with Group Search Optimization (PSGSO) to optimize the mined results from the Prefix Span method. We propose a new method called Frequent DNA Sequence Mining using Optimization (FDSMO) which combines Frequent Biological Sequence based on Bitmap (FBSB) and Hybrid of Firefly and Group Search Optimization (HFGSO) in this paper. The FDSMO process includes three stages: (i) applying the Frequent Biological Sequence based on Bitmap (ii) calculate length, width and regular expression (iii) optimization using HFGSO. The exploratory results demonstrate that FDSMO performs better than the existing methods, both in terms of running time and scalability.

10

Authors: Madhwaraj Kango Gopal

Software metrics play an important role in the planning and control of software development. The quality of the software is the degree to which a finished software product proves to be efficient to its specification. Recently, Object-Oriented (OO) methodology has emerged as a major approach for the software development in both scientific and business applications. As the necessity for productive software is increasing, the OO design technique for constructing software is proving to be a powerful method for developing efficient software systems. Generally, the software used by the organizations and the individuals is the one that is owned by the organization which develops it. But open source software is the one which is available free for the user and can be altered based on their needs. In such kind of the software, the maintainability and reliability play a major role regarding the quality of the software developed. In this paper, we have empirically analyzed the various design metrics of different versions of software using JDepend tool and their effect on maintainability is tested. Further, the reliability of the software is measured using Rayleigh’s model. The relationships between maintainability and reliability is found out by checking how these metrics influence the quality of software.

10

Authors: G Nagaraja, Pradeep Reddy Ch

Instant collaborative group communication can be achieved by deploying Mobile Ad Hoc Networks (or MANETs) without any pre-plan and pre-existing infrastructure setup. However, the curbs thrown by these networks, motivate the necessity of a group key management framework to secure data traffic. In this context, significant research work done in the last decade and proved that the trust based frameworks deliver better performance than others. In MANETs, trust quantified as the belief held by a node about another node. It is evaluated based on the assessing node direct experiences with assessed node itself, if they are neighbors and recommendations from remote nodes in the network with which assessed node has a direct experience, if they are remote. However, the framework based on recommendations by other nodes in the network might ultimately lead to system failure due to the menace of unfair recommendations by selfish and malicious nodes. Lying and on-off attacks on the system are such type of attacks. This motivates us to work on a framework which is immune to aforesaid attacks and should be lightweight due to node’s has limited computing resources. We propose a framework which scrutinizes dishonest recommendations based on the trust threshold value of remote nodes, distance from mean of trust recommendations and by maintaining a history of recommendations. Our simulation outcomes prove that proposed framework can deliver better network performance and resistance to lying and on-off attacks.

12

Authors: M. Parimala, Daphne Lopez

Spatial data mining is a rapidly growing field for analysing the data related to space and time. Nowadays most of the applications are based on these factors, so numerous data mining algorithms are developed for spatial characterization and to analyse the spatial trends. The spatial trend analysis determines the change in pattern of some non-spatial attributes on neighbourhood objects. In this paper, we identify spatio-temporal mobility pattern on the dynamics of Epidemic disease (H1N1) that plays a significant role in analysing the outbreak of an infectious disease. Modelling the transmission among the human population with respect to time and space leads to improved understanding of transmission mechanisms. A compartmental model is designed to characterize the disease dynamics of a random variable extracted from binomial and multinomial distribution. ArcGIS tool is used to visualize the mobility distribution of the infected host spatially and yields an output of frequent mobility locations with respect to different time slices. The results thus obtained would help the district administrative authorities to take strategic decisions and prevent the spread of the disease.