International Journal of Intelligent Engineering and Systems

10

Authors: Shailaja Acholli, Krishnamurthy Gorappa Ningappa

The security and confidentiality of the information has become a significant factor in the communication field. Several techniques for encryption and decryption are proposed to promote the security of the communication systems. Most powerful and significant part of the encryption is a key generation. Presently, hackers are able to break the key with help of the modern high computing machines. In this research, hybrid cryptographic algorithm - Extended Tiny Encryption Algorithm (XTEA) combined with International Data Encryption Algorithm (IDEA) was implemented for improving the security in real-time applications. A large key size ensures the randomness, but proportionally maximizes the network load with high complexity. To overcome this problem, the random numbers were generated using a Linear Feedback Shift Register (LFSR) scheme for key function. This algorithm was appropriate for encryption and decryption of online streaming data. The proposed method is named as International Data - Extended Tiny based Encryption Algorithm –LFSR (ID-XT-EA-LFSR) method. The ID-XT-EA-LFSR method improves the FPGA performances up to 50.43% compared to the QTL, DROM-CSLA-QTL and XTEA algorithms.

31

Authors: Kelly Rossa Sungkono, Ulva Erida Nur Rochmah, Riyanarto Sarno

Process discovery obtains a process model of activity records. There are two representations of process model, i.e. a probabilistic model and a deterministic model. A deterministic model takes all of activity records to depict a process model, however, the probabilistic model chooses several activity records that satisfy a threshold. Determination of the right threshold leads the emergence of many discovery algorithms of probabilistic models, such as Heuristic Miner, Fodina, Modified Heuristic Miner, and Modified Time-Based Heuristic Miners. Those algorithms determine a threshold based on users or an average of probabilities of activity records, so the quality of the model depends on user proficiency or frequent activities. This paper proposes a new algorithm of probabilistic model discovery, i.e. Heuristic Linear Temporal Logic (HLTL), which determines the threshold based on four quality aspects, i.e. Fitness, Precision, Generalization, and Simplicity. HLTL utilizes Linear Temporal Logic to create a formal representation of process model and store the weight of relationships used for the threshold formation. The result shows that the process model constructing by HLTL has better quality aspects than the process model constructing by Modified Heuristic Miners and Modified Time-Based Heuristic Miners. The generalization value of HLTL is 0.8422 and the generalization value of Modified Heuristic Miner and Modified Time-Based Heuristic Miners are 0.8421.

50

Authors: Sabah Sayed, Mohammad Nassef, Amr Badr, Ibrahim Farag

The challenge of processing the Microarray datasets with its high dimensionality opened multiple research directions. Different feature selection techniques have been employed to reduce the dimensionality of such Microarray datasets before being attempted by classification algorithms. This study presents an ensemble feature selection approach based on t-test and Genetic Algorithm with five different classification algorithms as its fitness function: Support Vector Machine, Random Forest, Nearest Centroid, K Nearest Neighbour, and Maximum Likelihood with 5-fold cross validation. The proposed approach has been applied on two different datasets for Lung cancer; Microarray Gene Expression and DNA methylation datasets aiming to find the Lung cancer biomarker genes. The experimental results showed that the three genes (DLX5, KRT5, and SELENBP1) resulted from processing both datasets have higher classification accuracy (92.31%) compared to separately processing the Gene Expression and the DNA methylation datasets with accuracies 90.38% and 86.54% respectively. Moreover, the classification accuracy achieved using the three aforementioned genes could not be achieved by other research studies unless by using more genes.

71

Authors: Deepti Sehrawat, Nasib Singh Gill

Lightweight security algorithms are tailored for resource-constrained environment. To improve the efficiency of an algorithm, usually, a tradeoff is involved in lightweight cryptography in terms of its memory requirements and speed. By adopting several performance enhancement techniques, a security framework for IoT enabled applications is presented in this paper. Proposed BRIGHT family of ciphers is comparably better than existing lightweight ciphers and support a range of block and key sizes for constraint environment. It enables users to match their security needs with application requirements by supporting a range of cryptographic solutions. The BRIGHT family of ciphers is a software-oriented design. The performance of BRIGHT family of lightweight ciphers is evaluated on different parameters. All versions of BRIGHT family ciphers fulfill Strict Avalanche Criteria, key sensitivity test, and randomness test. BRIGHT family ciphers show better performance in terms of memory requirements, cost and speed as compared to existing lightweight ciphers.

90

Authors: Bommalingaiah Nanjappa Mohan Kumar, Rangaraju Hulivangala Gangappa

Finite Impulse Response (FIR) filter is a major building block in Digital Signal Processing (DSP) system. The conventional FIR filters are implemented by utilizing a normal adder, which consumes more area. Minimizing the hardware utilization of the adder is a significant challenge in FIR filter design. Generally, complexity of the FIR filter is dominated by the adder. So, an efficient FIR filter was designed with the help of Carry Select Adder (CSLA) to reduce area and hardware complexity of the accumulation block. The area has been reduced by using CSLA that is very significant idea while designing a less area filter. The proposed method named as Low Area- CSLA-FIR (LA-CSLA-FIR) filter. The LA-CSLA-FIR filter was implemented in Xilinx Field Programmable Gate Array (FPGA) on different devices such as Virtex-4, Virtex-5 and Virtex-6 by using Verilog code. The experimental results of the LA-CSLA-FIR filter design reduced average the FPGA device utilization: 15.38 % of LUTs, 8% of flip flops and 8% of slices on Virtex-5 compared to existing filter designs.

111

Authors: Sara Diouani, Hicham Medromi

Given the development of the Cloud Computing recently, clients and customers using the Cloud for both individual and business needs have expanded to an uncommon scale. This has normally prompted the expanded deployments of Cloud data centers over the globe. As a result, Cloud data centers are seen to be monstrous energy consumers and natural polluters. They require an extraordinary measure of regular energy which has made an effect on the energy supply and natural conditions of the environment. This is the reason why the vulnerability of persistent energy supply, later on, is being referred to. In this way, there is a need of an energy-aware cloud-based system which automatically and efficiently manages and optimize cloud computing data center resources by considering energy consumption as an essential Quality of Service (QoS) parameter. This paper, focus on the energy utilization of the data centers and how this can be limited so as to make the cloud computing greener. Thus, a new autonomic resource optimization manager has been proposed to avail the most optimum level of resources with reduced server energy consumption. The proposed framework has been verified theoretically and tested experimentally. The experimental analysis has demonstrated that the effectiveness of the proposed solution is greater than the state-of-the-art methods in terms of the achieved results related to reducing energy consumption and response time in cloud computing data centers.

142

Authors: Reza Amalia Priyantina, Riyanarto Sarno

Product reviews are usually determined by sentiment of customers; however sentiment analysis based on aspects still need further research. A hotel commonly has five aspects, which are location, meal, service, comfort and cleanliness. This research proposes methods to determine review sentiment according to the hotel aspects. A hotel reviews are preprocessed into a term list. Firstly, Latent Dirichelet Allocation (LDA) determines the hidden topics of a term list; then Semantic Similarity categorizes the term list based on the topic resulted by Latent Dirichelet Allocation (LDA) into the five aspects of a hotel. Then in calculating similarity measurement, the term list is expanded by using the Term Frequency-Inverse Cluster Frequency (TF-ICF) method. Finally, a classification of customer sentiment (satisfied or dissatisfied) is conducted by using the combination of Word Embedding and Long-short Term Memory (LSTM). The results show that the proposed method can classify the reviews into the five hotel aspects. The highest aspect categorization performance is obtained by using LDA + TF-ICF 100% + Semantic Similarity which reaches 85%; the performance sentiment classification for the highest aspect-based sentiment analysis is obtained by using Word Embedding + LSTM which reaches 93%; and the comfort aspect receives more negative sentiments compared to the sentiments of other aspects. Also the results show that a sentiment is influenced by an aspect.

169

Authors: Youness Abouqora, Omar Herouane, Lahcen Moumoun, Taoufiq Gadi

The interest point detection is a technique for reducing the amount of data that needs to be processed in certain applications, this technique is widely used in 2D and 3D image analysis. In this paper, we introduce a new method for detecting the mesh interest points based on geometric measures. The key point of this new approach is to extract a satisfactory 3D interest points with a high repeatability and relevant distribution of the whole object. It consists of two steps: an interest point detection and a refinement post-processing. First, we compute the Harris operator function, that is robust to several transformations, around each vertex to selects the maximal responses as candidates of 3D interest points. Second, we use the shape index-based optimization method to select the final interest points with high curvedness. The performance of the proposed method is evaluated and it proves encouraging results.

192

Authors: Md. Yusuf Mulge

Task Scheduling (TS) is used to improve the performance of Cloud Computing (CC) and optimizing the Quality of Service (QoS) of the cloud. The main objective of this research work is to optimize the TS by minimizing the MakeSpan (MS) and energy consumption. The scheduling issues are overcome by Modified Mean Grey Wolf Optimization algorithm (MGWO) and it enhances the overall performance of the system. In this MGWO, the leadership hierarchy is simulated by four grey wolves such as alpha, beta, delta and omega for the major steps like hunting, searching, encircling and attacking the prey. Here, hunting and encircling modified using the mean value to improve the effectiveness of GWO. CloudSim toolkit is used for evaluating the objective of the Modified-MGWO in standard workload (left-skewed & right-skewed dataset). The proposed Modified-MGWO algorithm reduced energy consumption nearly 10% when compared with the existing algorithms such as Particle Swarm Optimization (PSO) and standard GWO. Moreover, the MS of the Modified-MGWO method reduced nearly 13% when compared with existing methods for both datasets.

212

Authors: Mariya Ouaissa, Mariyam Ouaissa, Abdallah Rhattoy

The Internet of Things (IoT) is a new concept that is developed considerably in current wireless communications. The main idea of this paradigm is the exchange of information and data from real-world devices with the internet by using unique addressing systems can communicate and interact with each other to achieve common objectives. With the emergence of next generation battery-powered of smart mobile phone, open source application platforms, security and the terminal’s energy consumption have become big issues. Long Term Evolution (LTE) is one of the most popular 4th Generation (4G) technologies defined by 3rd Generation Partnership Projects (3GPP) also IP Multimedia Subsystem (IMS) is a prominent architectural framework for multimedia services delivery in 4G/5G networks. In this paper, we focus on IoT security in particular, we explore authentication that is a critical security mechanism which accords authorized users access to a network. It is observed that the end user requires two authentication steps to access multimedia services. The first is the LTE network layer authentication, and the second is the IMS service layer authentication. The authentication steps utilize energy and are carried out using the Authentication and Key Agreement (AKA) protocol. As defined by 3GPP, IMS- Authentication and Key Agreement protocol (IMS-AKA) is the official authentication procedure in IMS and the standard EPS-Authentication and Key Agreement protocol (EPS-AKA) is proposed to provide the authentication service between the device and the network in Evolved Packet System (EPS) system. However, the procedures are prone to different weaknesses both on security and performances aspects. This paper proposes a secure and efficient AKA protocol that authenticates the user on both the network layer and the service layer without double execution the AKA protocol, simplifies the authentication steps, protects user’s identities and reduces the authentication process complexity due to the use of Elliptic Curve Cryptography (ECC). The proposed solution was checked by the security protocol verification tool, Automated Validation of Internet Security Protocols and Applications (AVISPA), which indicated that it is a very secure level. Simulation results showed that our proposed compared to IMS-AKA and EPS-AKA offers better performances in terms of energy consumption and storage cost.

233

Authors: Mohammad Nasrul Aziz, Tohari Ahmad

Various machine learning technology approaches have been applied to intrusion detection system (IDS). To get optimal results, it needs to take several stages for processing the traffics. Among them is the feature selection method, where irrelevant and redundant features are removed. In the previous research, the system is developed based on feature grouping that used a clustering approach as the evaluation criteria. In this research, we propose a method for improving the performance of machine learning with the feature selection approach based on feature clustering. We propose cluster based feature selection derived from the value of mutual information and Pearson correlation. The cluster hierarchy is used in forming filters that are used to create selected and reduced clusters. In developing the cluster hierarchy, single, complete, and average linkage method are used to determine the formation of the best feature clusters. The classification method with Support Vector Machine (SVM), Naïve Bayes, and J48 decision tree are applied to observe the performance of the proposed feature selection. Based on the experimental results, we find that the highest accuracy (i.e., 99.842%) is obtained when a single linkage in the J48 classification is implemented in the Kyoto 2006 dataset.

255

Authors: Varinder Kaur Attri, Jatinder Singh Bal

Software Size Estimation is the most essential and crucial calculations of the Software Development Life Cycle (SDLC) process. If done wisely, it may accumulate a large amount and time and if done badly, it may cost a lot of amounts. In this modern era of development, traditional methods of estimation do not stand a chance to provide output precisely. Constructive Cost Model (COCOMO)-2 is one of finest calculation methods for size and cost. This paper presents an automated early size estimation technique using Artificial Intelligence (AI). The categorization of the size estimation is into three parts, that is, UML diagrams/code folder mapping via COCOMO-2, COCOMO-2 metrics training, and the classification process to have an appropriate size. This paper focused on regression based training of AI which makes the estimation model more precise. For the evaluation, Mean Square Error (MSE) and Size estimation have been considered according to project samples. It has been seen that the proposed mechanism has attained a minimum MSE of 0.0115 and the difference in sizes is also not high. The comparison has also been done to depict the efficacy of the proposed work with A. B. Nassif et al. and S. Lohmor with B.B. Sagar for True positive rate and Mean square error.

275

Authors: Dewi Ayu Khusnul Khotimah, Riyanarto Sarno

In the industrial era 5.0, product reviews are necessary for the sustainability of a company. Product reviews are a User Generated Content (UGC) feature which describes customer satisfaction. The researcher used five hotel aspects including location, meal, service, comfort, and cleanliness to measure customer satisfaction. Each product review was preprocessed into a term list document. In this context, we proposed the Probabilistic Latent Semantic Analysis (PLSA) method to produce a hidden topic. Semantic Similarity was used to classify topics into five hotel aspects. The Term Frequency-Inverse Corpus Frequency (TF-ICF) method was used for weighting each term list, which had been expanded from each cluster in the document. The researcher used Word embedding to obtain vector values in the deep learning method from Long Short-Term Memory (LSTM) for sentiment classification. The result showed that the combination of the PLSA + TF ICF 100% + Semantic Similarity method was superior are 0.840 in the fifth categorization of the hotel aspects; the Word Embedding + LSTM method outperformed the sentiment classification at value 0.946; the service aspect received positive sentiment value higher are 45.545 than the other aspects; the comfort aspect received negative sentiment value higher are 12.871 than the other aspects. Other results also showed that sentiment was affected by the aspects.

306

Authors: Ahmad Ilham, Romi Satria Wahono, Catur Supriyanto, Adi Wijaya

Determining the cluster number in k-means is problematic since it affects the quality of cluster for numerous applications in the data mining. The automatic clustering differential evolution (ACDE) is one of the most used clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine a value k activation threshold thereby affecting its performance. In this study, the u-control chart (UCC) method use to tackle the ACDE method problem. UCC method used for the initial step to get a value of the variables sought before initialization of the variable vector on ACDE. The UCC is a method from statistical process control (SPC) field which has proved to be effective in solving the problem of management control attributes. The performance of the proposed method was tested using seven public datasets from the UCI repository and Clustering basic benchmark repository and evaluated with Davies Bouldin Index (DBI) and CS measure. The results show that, the proposed method yields excellent performance compared to prior researches for most datasets with optimal cluster number yet lowest DBI and CS measure. It can be concluded that the UCC method is able to determine k activation threshold in ACDE that caused effective determination of the cluster number for k-means clustering.

328

Authors: Chellan Edward Jaya Singh, Eppies Baburaj

In this modern world, most of people are using cloud computing due to its accessibility. Users are uploading plenty of images to the cloud every day. But still, data confidentiality is the major issue for the cloud storage. This paper proposes a new method of image encryption scheme with less storage in the cloud. The proposed method performs double encryption with de-duplication over the images for better security and also reduces the storage overhead problem using the scaling and cropping operation. In the first level, the modified Paillier encryption technique is used and an XOR encryption takes place in the second phase. After that, a secure block-level image de-duplication technique is performed to eliminate the identical images and protect the image confidentiality. The simulation result exhibits that the proposed encryption with secure de-duplication outperforms other state-of-art approaches with respect to the performance metrics like accuracy, sensitivity, specificity, encryption quality and runtime. In terms of accuracy, the performance of proposed method is better of 8.1%, 7.2%, and 6.1% for image 1, 10.2%, 7.1%, and 6.1% for image 2, 8.4%, 6.2%, and 5.8% for image 3, and 8.3%, 6.2%, and 5.6% for image 4 when compared with other three existing research works.

348

Authors: Shaik Khaleelahmed, Nandhanavanam Venkateswararao

Enhancing the efficiency of power allocation and the achievable sum rate of Multiple Input Multiple Output (MIMO) based multi-user systems are essential as conventional methods are very complex. The traditional methods are not only less efficient but also do not address user fairness. The existing conventional methods use simple user ordering schemes that remain unsuitable for MIMO Non-Orthogonal Multiple Access (NOMA) system. Accordingly, a new elegant propose method using Particle Swarm Optimization (PSO) algorithm based priority scheduling in power allocation (PPPA) prioritizes the users based on the maximum power and QoS constraints. The proposed method offers a capable platform that will also provide energy efficiency in power allocation. The priority-based scheduling based on PSO algorithm that prioritizes the users optimally based on objective function, and it is effective due to faster convergence and adaptive nature of the algorithm. The proposed method for power allocation compared with the existing methods using the number of admitted users in the system. We observed that the energy efficiency and achievable rate is found to be superior by 6.76% and 58.37 Mbits/sec. Thus our proposed method not only provides better energy efficiency but also has a profound improvement in data rate.

367

Authors: Yassir Rochd, Imad Hafidi

Frequent item exploration is a fundamental element in many data mining problems aimed at finding interesting models in the data. Recently, the PrePost algorithm, a new algorithm for extraction frequent element sets based on the idea of N-lists, which in most cases surpasses other current state-of-the-art algorithms, has been introduced. The PrePost algorithm's performance deteriorates when it comes to handling big data. Nevertheless, the current existing PrePost algorithms in place implemented with the MapReduce model are not sufficiently powerful for iterative computation. To reduce IO overhead and take advantage of cluster memory, this article offers an enhanced version of PrePost, the Distributed PrePost (DisPrePost), a parallel algorithm built on the Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation, that also utilises a HashMap to further refine the N-list creation process. Experience has shown that the DisPrePost algorithm is more efficient and scalable than the two advanced state-of-the-art methods HPrePostPlus and the well-known algorithm HFIM.

11

Authors: Tossapol Kiatcharoenpol, Thanakarn Vichiraprasert

Design of Experiment (DOE) has prevailingly used in various industries from its effectiveness of a statistical technique for managing and improving processes. In this work, the three DOE approaches, Classical DOE or factorial design, Taguchi method and Shainin DOE are applied in a case of plastic injection molding process. A comparative analysis is carried out to express the way of implementation and the merit of each approach based on the accuracy of information and the ease of use. The results show that significant factors obtained from the three DOEs are aligned together. For all approaches, the most three factors influencing to the response, %volume shrinkage, are exactly the same including the optimal process parameters chosen. This is one of an evidence of effectiveness for both Taguchi method and Shainin DOE in practice. The adoption of DOE approaches outlined in this paper and their application to an industrial process will be valuable to practitioners to gain understanding of DOE techniques and to use them fit to their quality issue.

31

Authors: Lukman Hakim, Riyanarto Sarno, Kelly R. Sungkono

Process discovery is a concern in analyzing business processes. Process discovery methods handle many aspects, e.g. a hybrid of non-free choice and invisible task. A hybrid of non-free choice and an invisible task is a condition that needs additions of invisible tasks in non-free choice relationships. Process discovery methods, e.g. Alpha++, Alpha#, Graphical method, Time-based Alpha Miner, cannot discover the hybrid of non-free choice and invisible task, especially invisible tasks in the overlapping pattern. To modeling a hybrid of non-free choice and invisible tasks, this research proposes an algorithm of adding invisible tasks, including invisible tasks of overlapping pattern, automatically in the event log and processing the event log with the rules of Alpha++ algorithm. To test the proposed algorithm, namely Invisible Task Log with Alpha++, the experiment compares it with Alpha++, Alpha#, Graphical Method, Time-based Alpha Miner, Hidden Markov Model-Parallel, and Coupled Hidden Markov Model-Invisible Task. The experiment results proved that Invisible Task Log with Alpha++ algorithm obtains higher precision than the obtained precision by comparison methods.

50

Authors: Rokesh Kumar Yarava, Rajendra Prasad Singh

Cloud storage auditing technique helps to verify the data integrity in the cloud on behalf of the user. Earlier researcher proposed many techniques on the cloud auditing, but it still lags in computational overhead for the resource constraint users. In this research, the Diffie-Hellman method is used to exchange the key in the Third Party Auditor (TPA) to improve the performance of the auditing method. The key is generated in the third party auditor and this is shared with the user as an encrypted key. The Diffie-Hellman creates the shared encryption key between the two parties and can be communicate in the insecure channel. The Diffie-Hellman generates ephemeral keys and this is extremely fast in generating new key pairs. This method is suitable for the big server and have more efficiency over the state-of-art method, which doesn’t involve in ephemeral key generation. The proposed method is evaluated in the simulated environment and compared with the other state-of the art methods. The verification cost of the proposed auditing method has achieved 1.1 s, while the existing method has 1.19s.

68

Authors: Archana Kalidindi, Prasanthi Yavanamandha, Anusha Nagina Kunuku

The recommendation system plays a major role in e-commerce services and online applications such as social-networking, product recommendation, etc. The recommendation system uses the most popular techniques namely Collaborative Filtering (CF) and Deep Learning Neural Network (DLNN) approach. The Cold-Start (CS) problem and the recommendation efficiency are considered as crucial challenges and affected the efficiency of the most popular techniques. In this paper, a Discrete Deep Learning (DDL) based on hashing framework is implemented to achieve Hamming space for mapping Users and Items (UIs). Hamming distance used here to calculate the user’s preference for an item, and the efficiency of online recommendation significantly improved by this computation technique. The performance of DDL framework evaluated by conducting various experiments on the large Netflix rating dataset. The experimental outcomes showed that online recommendation efficiency and CS recommendation accuracy of DDL outperformed the existing methods.

88

Authors: Agus Urip Ari Wibowo, Selo Sulistyo, I Wayan Mustika

Probabilistic Routing Protocols using History of Encounters and Transitivity (PRoPHET) assume that nodes do not move randomly but encounter destinations that have been encountered before. In some original PRoPHET and original PRoPHETv2 testing, they are superior to other routing protocols. Testing in environments that use trains as relay nodes, original PRoPHET performance is worse than the epidemic. This happens because messages delivery to other relay nodes are assumed to encounter the destination (having a larger probability of encountering destination) while the other relay nodes are moving away. The original PRoPHET and the original PRoPHETv2 performance can be improved by delivering a message to relay nodes that will encounter the destination. This can be done by delivering a message to relay nodes with lower probability values encountering the destination. The result obtained is a modified PRoPHET and a modified PRoPHETv2 performance improvement compared to the original PRoPHET and the original PRoPHETv2.

108

Authors: Arna Fariza, Agus Zainal Arifin, Eha Renwi Astuti, Takio Kurita

Tooth component segmentation is a crucial task in computer-aided design for forensic odontology, especially to estimate chronological age. Tooth segmentation on radiographic data is a very challenging task due to noise, low contrast, and uneven illumination. The Fuzzy C-Means clustering is generally used for image segmentation that allow pixels to be classified into one or more clusters according to their membership value. However, this clustering method still has problems associated with the shifting of cluster centers and sensitivity to the overlapping intensity distributions between classes. This paper proposes a modified strategy of the conditional spatial Fuzzy C-Means (csFCM) that incorporates global and spatial information into a weighted membership function by replacing the Euclidean distance with the Gaussian kernel distance to increase insensitivity to noise and outliers. The aim of this paper is to divide the tooth into 3 components, i.e. enamel, dentine, and pulp. Therefore, this modified algorithm is preceded by dental X-ray image pre-processing and continued by combining each dental component clusters into one composite image. The tooth image is pre-processed using Contrast Limited Adequate Histogram Equalization (CLAHE) and gamma adjustment to enhance the dental X-ray images quality from the non-uniform lighting. The Gausian kernel-based conditional spatial Fuzzy C-Means (GK-csFCM) segments the dental image into four class clusters, namely enamel, dentine, pulp, and background. Through iterations, the resulting cluster centers are more convergent with real cluster centers, thus ensuring the proposed method improves the drawback of inherent FCM-based methods and further promoting image segmentation performance. The experimental results on the real dental X-ray images showed that GK-csFCM has better performance than the typical FCM and csFCM clustering algorithms in terms of both qualitative and quantitative metrics, i.e. accuracy, specificity, sensitivity, and precision.

129

Authors: Ahmad Saikhu, Agus Zainal Arifin, Chastine Fatichah

Data relating to everyday phenomena can be recorded in the form of time series. Recently, modeling of time-series has become a topic of interest in data mining. Conducting an analysis of multivariate time series effectively is an essential task for decision-making activities in fields such as meteorology, medicine, and finance. Features selection is a key problem in the analysis of multivariate time series. Rainfall prediction, biomedical classification, pattern recognition, sensor network analysis and so on all have different input features. The problem is that these features have interdependencies and time-delay relationships. Currently, research on the selection of input features of these data still depends on whether they are linear or non-linear. In this paper, we propose a new integration strategy between Pearson Correlation and Symmetrical Uncertainty for relevant feature selection based on linear and non-linear relationships for multivariate time-series classification. We evaluated the goodness of fit of feature subsets using merit value. The meteorological data set was used to test the proposed method. The result showed that the method was able to reduce the number of features by 77.9% features and increase their merit value 2.25 times compared to no input features selection.

148

Authors: Gundugonti Kishore Kumar, Narayanam Balaji, Kotha Srinivasa Reddy, Vemu Thanuja

This paper presents a modified 2D Discrete Wavelet Transform (DWT) architecture with a proposed 16-bit Radix8 Booth multiplier. Existing architecture makes use of Canonic Sign Digit (CSD) representation and when replaced the CSD multiplier with the proposed 16-bit Radix8 Booth multiplier it achieves better performance with small area and low power. In proposed Radix-8 Booth multiplier, the necessary product terms are generated and the remaining terms are truncated. In this method, the n order bit required by the specific coefficient is obtained and the remaining n bits are truncated so that 2n bit output truncated to n bit. The modified 2D DWT architecture is proposed to enhance that it occupies less number of clock cycles, so that it improves in the speed of operation By comparing synthesis results for existing CSD multiplier and the proposed Radix-8 Booth multiplier achieves an improvement of nearly 29.02% Area Delay Product (ADP) and 26.13% Power Delay Product (PDP).

166

Authors: Majeti Venkata Sireesha, Panchala Venkata Naganjaneyulu, Kaparapu Babulu

To depict the hyperspectral data, here a sturdy mixing model is implemented by employing various perfect spectral signatures mixture, which enhances the generally utilized linear mixture model (LMM) by inserting an extra term that describes the potential nonlinear effects (NEs), which are addressed as additive nonlinearities (NLs) those are disseminated without dense. Accompanying the traditional nonnegativity and sum-to-one restraints underlying to the spectral mixing, this proposed model heads to a novel pattern of sturdy nonnegative matrix factorization (S-NMF) with a term named group sparse outlier. The factorization is presented as an issue of optimization which is later dealt by an iterative block-coordinated descent algorithm (IB-CDA) regarding the updates with maximation-minimisation. Moreover, distinctive hyperspectral mixture models also presented by adopting the considerations like NEs, mismodelling effects (MEs) and endmember variability (EV). The extensive simulation analysis by the implementation of proposed models with their estimation approaches tested on both the synthetic and real-time images. Further, it is also shown that the comparative analysis with the conventional approaches.

192

Authors: Sanjive Tyagi, Rakesh Kumar Dwivedi, Ashendra K. Saxena

In this paper, a novel approach of PDF-based text steganography is presented. A covert communication is achieved by embedding secret information at between-characters positions of words within cover PDF text. A stego-encoding technique is being designed to improve the embedding capacity along with sufficiently reduced overheads of stego-cover PDF file. The proposed method constructs multilevel embedding capacities which can be used according to requirements from higher embedding capacity to typical embedding capacity. Encoding and decoding of secret text is being obtained by implementing quadratic probing of hashing technique that builds up a direct encoding table which provides better performance in term of time complexity. Exploratory outcome exhibits that the proposed technique gives productive algorithms in term upgraded security and high payload of concealed data. Experiential verification and examination of the proposed technique with certain prominent text steganographic techniques demonstrate that proposed technique performs better than current methodologies and free of requirements like shading scheme, languages, wordlist, compression and so forth.

214

Authors: Mouna Berquedich, Oualid Kamach, Malek Masmoudi

The management of emergency services within hospitals typically requires an effective manipulation and capitalization of data sources comprising worth information useful for hospitals’ security, safety and efficiency. To meet these requirements, we dispose an agile approach of decision-making that addresses massive crowds occurring at the emergency departments. This approach considers the major constraints that might face these departments, as such lacks of human resources, high costs, and confusedness of patient’s prioritization cases, non-sufficient capacity and logistics. We, basically, inspired from the biological immune defence processes to illustrate a piloting emergency mean known as the artificial immune system (AIS). This system intelligently assists hospital’s decision-makers to enhance their supplying strategies, provides relevant traces from tracking previous information that assist hospital’s staffs that face the massive patient flow, and fosters the execution of efficient solutions. This paper investigates AIS data and relevant traces of overcrowding dilemmas, in which these data could be exploited to increase the reception planning, decrease tension-related anomalies within the emergency department, and supplying hospital chiefs working under stress. The main objective is to present the basic concepts of a new approach that aims to provide hospital decision-makers with relevant traces to assist them in their decision-making in the difficult situations. The expected benefits (main findings) for using the considered approach are not only to provide crisis managers with a relevant computerized decision support system, but also to minimize financial costs, reduce the response time and positively impact the crisis management.

244

Authors: Ahmed Abdelaziz, Nagy Ramadan Darwish, Hesham Ahmed Hefny

The management of agile software projects plays an important role in the success or failure of projects. There are many factors of success or failure that clearly affect agile software projects. There is a big problem in not having a formal study to identify critical failure factors that assess the success or failure of projects accurately. This paper focuses on failure factors of agile software projects. This paper seeks to configure a formal study by using multiple regression analysis for identifying critical failure factors to avoid projects failure, thus saving time and money for the stakeholders. The proposed model is implemented by using R programming. The proposed model also is implemented by two models. The first model based on critical failure factors (CFF) of agile software projects. The second model based the failure factors of the proposed in the initial list (FFIL). The model of CFF outperforms on the model of FFIL in order to accuracy by 10.7% and random error ratio by 4.8%. The results revealed that only 10 out of 19 hypotheses were supported, identifying seven critical failure factors for Agile software development projects: (a) Organizational culture too traditional, (b) Lack of agile logistical arrangements,(c) Lack of project management competence, (d) Lack of team work, (e) Bad customer relationship, (f) defined project scope, and (g) defined project planning.

268

Authors: Dipti Yashodhan Sakhare

Nowadays, Motion Estimation (ME) plays a vital role in traffic signals, movie making, and security purpose to identify the motion of an object. In this paper, Smart and Low power (SLP) based ME is introduced with Level Convertor and Carry SeLect Adder (LC-CSLA-ME). Normal SLP architecture requires more power for performing ME, but in the LC-CSLA-ME architecture, 0.9v supply voltage is used to carry-out the ME. The LC is used to supply the required voltage to the respective sub blocks, which helps to reduce the power consumption of the overall architecture. Instead of using normal adder, CSLA adder is used in prediction and classification unit for improving the hardware utilization. The performance of Application Specified Integrated Chips (ASIC) and Field Programmable Gate Array (FPGA) analysed for both existing and proposed methods. In 180nm technology, LC-CSLA-ME architecture occupied 0.065mm2 area, 2.10mW power, and 104ms delay. The different dataset such as Johnny, Vidyo 1, and Vidyo 4 is analysed for ME. The Coding Time Saving (CTS) evaluated for both existing and LC-CSLA-ME architectures. In the LC-CSLA-ME, average CTS of Operating Power Points (OPP1, OPP2, OPP3, OPP4, and OPP5) are 34.021, 54.316, 67.021, 75.027, and 80.071 that is high compared to the existing methods.

292

Authors: Kavikondala Praveen Kumar Rao, Tamilarasan Senthil Murugan

Mobile Ad-hoc Network (MANET) dynamically designed by the self-organization of the mobile nodes connected through wireless links without using any centralized administration. Energy consumption is the most significant issue in the MANETs, because most of the mobile host’s functions have inadequate battery resources. Reduction in the energy consumption increases the lifetime and throughput of the network. The performance of existing techniques is less in terms of energy conservation issues. To overcome this limits, this paper suggested an energy conservation mechanism incorporated with a pro-active MANET routing scheme. The routing schema is employed in the energy level and the movement of the nodes. Calculation of energy cost is accomplished based on the assessment of the energy consumption level of the node, using K-Means clustering along with AODV (Ad-hoc On Demand Distance Vector). The data packets are secured using (AES), while sending data from source to destination. The proposed methodology “K-Means-AODV-ACO-AES” model is compared with the existing Optimal Key Management for Secure Data Transmission (OKMSDT) and Secured AODV (Sec-AODV). From the comparison result, it is observed that the K-Means-AODV-ACO-AES” increases in Packet Delivery Ratio (5%), along with the decrease in end-to-end-delay (6%), and energy consumption (7%) and drop (8%) in secured environment.

319

Authors: G. R. Sakthidharan, P. Chandra Sekhar Reddy

There has been a tremendous growth in research concern in Wireless Sensor Networks (WSNs), due to the broad range of applications in distinctive areas such as environmental observation, security domains, home automation, etc. Despite advantages, the prevalent adoption of WSN, specifically in remote and isolated regions, has become the major challenge, because of this battery constrained network with limited energy source. As advancement, the nodes are framed under Wireless Rechargeable Sensor Network (WRSN) to enhance the lifetime of sensor nodes with the establishment of charging mechanisms. With those considerations, this paper presents a Schedule-based Optimized Node Recharging Model (SONRM), in accordance to the condition of limited capability of mobile charging. In the process of the construction of charging circuit, the SONRM effectively considers the dynamic changes of energy utilization among nodes at different factors and develops a Recharge Scheduling Algorithm (RSA) that helps in finding the next Energy Starving Sensor Node (ESSN) to be recharged. Simultaneously, a model for the establishment of feasible circuit for recharging has also been done for ensuring the effectiveness of charging. During the operation, the node mortality can be reduced by the proposed model with the incorporation of dynamic charging threshold. When compared with the existing mechanisms, the SONRM attains better results on analysing decisive factors such as network longevity, service time, efficient charging, throughput and recharging time.

12

Authors: Abdelhadi Ennajih, Badr Nasiri, Jamal Zbitou, Ahmed Errkik, Mohamed Latrach

In this article, we propose a new design of a miniature antenna for integration into the RFID (Radio Frequency Identification) reader and the RFID transponder to operate in the Moroccan and European UHF (Ultra High Frequency) band. The proposed antenna is designed on a flexible PET (Polyethylene Terephthalate) substrate. The proposed tag consists of a microchip and a dipole antenna loaded metamaterial based on a spiral split ring resonator which has a negative magnetic permeability in the UHF RFID Moroccan and European band. The metamaterial unit cell is used to reduce the antenna size and a double T-matching structure is inserted to match the dipole antenna with the “Monza 1a” chip. The antenna yields an omnidirectional radiation patterns and a gain of 2.2 dBi at the central frequency. The designed antenna has a small size about 63 mm x 14 mm which is suitable for paper boxes and bottles labelling.

33

Authors: Vijay Kumar Nadipinayakanahalli Krishnappa, Suresh Hosahalli Narayanagowda

Secure mechanisms are widely in Wireless Sensor Networks (WSNs), not only for improving the network energy consumption and lifetime, it also improves the security between the networked Sensor Nodes (SNs). Key management is a fundamental security mechanism which is equipped in WSN. In this paper, a Random Key Pre-distribution (RKP) scheme is used in Low-Energy Adaptive Clustering Hierarchy (LEACH) based clustered network. LEACH divides the network into a number of clusters and also it elects the optimum Cluster Head (CH) from each cluster. Then RKP scheme provides the common keys to each SN of the network to improve the secrecy of the network. Combination of LEACH and RKP scheme is named as LEACH-RKP-WSN. After performing key distribution, the data packets are sent from the source (anyone of SN) to Base Station (BS). The performance of LEACH-RKP-WSN was compared with Adequate Sparse Secure and Minkowski distance based Location Privacy (ASSMLP). Thus the result shows energy consumption, packet delivery ratio, throughput and delay and it achieves 12.1%, 2.25%, 6.5% and 2.25% better performance than ASSMLP. The lifetime of the network increased by minimizing the energy consumption of the LEACH-RKP-WSN.

53

Authors: Pardhasaradi Pokkunuri, Boddapati Taraka Phani Madhav, Gullapalli Kesava Sai, Manikonda Venkateswararao, Bonthu Ganesh, Nimmagadda Tarakaram, Dangeti Pavana Teja

The multiband reconfigurable fractal slot antenna which is having Split ring resonator and complementary split ring resonator is proposed in this article. The proposed antenna works at the bands of GPS (1.6GHz), Bluetooth (2.4GHz) and at the middle of the WiMAX bands (5.7GHz). In respect to that, the frequency reconfigurability of the proposed antenna has been tested and analysed by placing PIN diode which is attached to Complimentary Split Ring Resonator (CSRR) at the backside (ground) of the antenna. The switchable characteristics of the antenna are tested using two diodes. When ON condition the switching characteristics are seen in the lower bands such as 1.8GHz (B.W=18%) and WiMAX 3.5GHz band (B.W=20%).during OFF condition the lower frequency band of GPS is converted as the notch band and increase of about 20% B.W has been observed in Bluetooth 2.4GHz and 5.6 UPPER WiMAX bands. The compact antenna of size 35mm*30mm is designed and analysed using ANSYS EM Desktop. The gain and stable radiation patterns with radiation efficiency towards frequency have been observed in the article.

74

Authors: Pannir Selvam Ramila Rajaleximi, Mohammed Saleem Irfan Ahmed, Ahmed Alenezi

Fraud identification and prevention is becoming the decisive challenge for any financial institutions and financial service industries. Majority of the banks use a credit score, a numerical or statistical value to estimate the customer’s creditworthiness based on their credit history. A credit score model will be built by employing training dataset and further, an analytical process will be conceded to estimate the credit score of each customer. Thus, for any credit score model, the lenders acquire various data about the customer from various external agencies. The collected data may encompass irrelevant parameters which will not help in making any decisions and also decelerates the global performance of the model. Accordingly, feature selection is an imperative process to eliminate less relevant attributes for any dataset, especially in credit scoring. This paper emphasizes on enlightening the performance of the attribute selection process using multiple rank score model. The proposed method accumulates the results using the optimized threshold algorithm and outperforms well in selecting the quality attribute for the underlying credit score model. The experimental evaluation has been carried out and it is proved that the accuracy and performance of the suggested method are comparatively better than the existing single rank techniques.

95

Authors: Vysyaraju Lokesh Raju, Valluri Rajya Lakshmi, Mosa Satya Anuradha

A Compact triple notch band monopole antenna with defected ground structure is proposed in this article. Corner truncated square radiating element on the top side and defected ground structure on the bottom side are placed on FR4 substrate of permittivity 4.4. An inverted U-slot is placed on the feed line to have proper impedance between feed point and the ground plane. The proposed antenna providing triple notch band characteristics between 7-7.4 GHz, 8-12 GHz and 16-18 GHz respectively. The fabricated antenna occupying the dimension of 24X26X1.6 mm and the measured results providing excellent correlation with simulation results.

114

Authors: Ammar Abdulrahman Ahmed, Mona A. S. Ali, Mazen Selim

Nowadays, breast cancer considered a main cause of death for women all over the world. It is defined as a group of cells that grow rapidly and causes the formation of a lump in breast tissue which leads to tumor formation which can be categorized either malignant (cancerous) or benign (non-cancerous). On the other side, mammography as a screening and diagnostic tool suffers from some limitations, especially with young women who have dense breasts. Therefore, there was a need to develop more effective tools. Thermography is an imaging tool used to record the thermal pattern. The main contribution of this paper is proposing a unique method for classifying the breast thermography images into one of three classes: normal, benign, or malignant. Additionally, bio-inspired algorithms namely, ant colony optimization (ACO) and particle swarm optimization (PSO) are used for feature selection. The proposed method contains four phases: Image preprocessing, feature extraction, feature selection, and classification. The proposed method is assessed using a benchmark thermography dataset. The experimental results show that our method has a promising performance.

135

Authors: Senthil Kumar Thillaigovindan, Prabakaran Subramaniyan

In Wireless Mobile Networks (WMN), the proliferation of mobile devices and smart phones stimulates an array of personalized information services that exploits the user’s personal data for processing. So, it is very significant to preserve the data privacy and protect the integrity of data of mobile users. However, as the WMN devices are heterogeneous and highly independent, it is challenging to achieve privacy protection and efficient authentication in better levels. With those concerns, this paper illustrates a new model called Enhanced Privacy Preserving- Anonymity Authentication (EPPAA) for protecting the user’s personal information. Further, the model incorporates the effectiveness of Quantum- behaved Particle Swarm Optimization (QPSO) for selecting the node at middle of neighbours that are closer to the Serving Base Station (seBS). The ticket based anonymity authentication has been employed and the algorithm has been designed and implemented predominantly. For providing confidentiality over the communication, the query message is encrypted, by that way; the anonymous users could not claim the private data of the mobile users. Moreover, the proposed model is implemented and evaluated using the NS2 simulator. The experimentation has been analyzed with the parameters such communication overhead, authentication delay, success ratio, packet delay and compared with some existing privacy preserving models such as Kerberos based Authentication for Inter-domain Roaming (KAIR), Privacy Preserving Nearest Neighbor Queries(PPNNQ) and Efficient Mobile Authentication Scheme (EMAS). The results of the proposed EPPAA show that the model outperforms the traditional methodologies and provides better authentication and security to the user information on WMN.

156

Authors: Muthusamy Palani Rajakumar, Ramya Jegatheesan, Rashmi Chandy, Tanusshri Sampath

As more interest by the people about stock markets is growing substantially bigger, the more their thinking is directed to a systematic method to predict stock prices that vary. There is an increasing appreciation that unstructured qualitative data contain awfully precious information, helpful for a variety of reason. It is this motivation that has fascinated awareness of researchers and specialized into the interesting and demanding area of stock market prediction using qualitative news and events. In this paper, a framework for stock price movement prediction using news, company newsletter and financial information was presented. Financial information and events were processed and multilayer feed forward network trained using back propagation was employed to empirically explore the intricate relationships between events and the stock market prices. Among the various industries we chose to focus on the Information Technology (IT) sector since it is most prevalent and applicable to the Indian markets. The stock prices and the relevant unstructured information for six IT companies were collected and filtered based on set of keywords. Our research has proven that the stock price can be accurately predicted from news articles by furnishing the empirical results that were obtained after training the multilayer neural networks.

182

Authors: Vantaku Nagendra Lakshmana Kumar, Moturi Satyanarayana, Sohan Pal Singh

In this communication, back lobe reduction in microstrip antenna array using partial ground and defected ground structure (DGS) is presented. An eight element rectangular microstrip patch antenna array is designed for X-band frequency of 9.1GHz, with a peak gain of 12.04dBi and back lobe level of -13.75dB. Using partial ground plane technique; the back lobe level is reduced to -18.28dB. Then, by placing defected ground structure (DGS) slots on the ground plane, the S11 is further reduced by 7.65dB and back lobe level is reduced to -18.82dB. This microstrip antenna array with partial ground plane and DGS technique has a peak gain of 12.41dBi. By using this novel technique, the front to back ratio has improved from 19dB to 24.23dB. The Simulations are carried using High frequency structured simulator (HFSS) software and compared with measured results. A difference of 0.96dB is observed between simulated and measured back lobe level.

202

Authors: Sangamesh Hosgurmath, Viswanatha Vanjre Mallappa

For the past few decades, biometric Face Recognition (FR) is the active research area in different domains such as image processing, pattern recognition, etc. The existing FR system has several limitations such as Single Sample Problem, maximum Reconstruction Errors (REs), these problems decreases the FR rate. In this research paper, an efficient FR method is proposed, namely Grey Wolf Optimizer based Linear Collaborative Discriminant Regression Classification (GWO-LCDRC). The optimization technique of GWO algorithm is applied in LCDRC to select the relevant weight value in LCDRC. For every training sample, optimal weight values are selected to improve the recognition rate. The proposed GWO-LCDRC method maximize the collaboration of Between Class RE (BCRE) and minimize the Within Class RE (WCRE). An experimental analysis conducted on the two standard facial databases namely ORL and YALE. The proposed GWO-LCDRC method improved the performance of recognition accuracy with respect to different training samples. Also, the performance is compared with the traditional methods Linear Regression Classification (LRC), Linear Discriminant Regression Classification (LDRC), and LCDRC. The overall experiment demonstrated that the proposed GWO-LCDRC method achieved approximately 3% and 6.5% of FR accuracy improvement with respect to ORL and YALE respectively.

222

Authors: Agus Kiswantono, Eko Prasetyo, Amirullah Amirullah

This paper presents comparative performance between Maximum Power Point Tracking-Fuzzy Mamdani (MPPT-FM) and MPPT-Fuzzy Sugeno (FS) methods as controller on Photovoltaic (PV) output power using Dynamic Voltage Restorer-Battery Energy Storage-PV (DVR-BES-PV) system connected to three phase three wire (3P3W) distribution network. The combination of DVR-BES-PV system using two methods is used in order to mitigate voltage sag/swell and harmonics on load bus. The PV is used as an alternative DC voltage source to charge BES when its capacity decreases and provides active power needed to compensate for sag/swell. In nine sag/swell disturbances scenario, MPPT-FM is able to give better performance in percentage of sag/swell on load voltage than MPPT-FS. In 90% voltage sag, MPPT-FM is able to result percentage of voltage sag on load bus of 5.759% smaller than MPPT-FS of 5.770%. Then, in 190% voltage swell, MPPT-FM is able to result percentage of voltage swell on load bus of 0.892% smaller than MPPT-FS of 1.064%. Both methods are also able to result percentage of sag/swell on load voltage under IEEE 1159. In voltage sag, MPPT-FM is able to result an average Total Harmonics Distortion (THD) of load voltage higher than MPPT-FS. Otherwise in voltage swell, MPPT-FM is able to result an average THD of load voltage smaller than MPPT-FS. In 90% voltage sag, MPPT-FM is able to result average THD of load voltage of 10.32% higher than MPPT-FS of 10.14%. Otherwise, in 190% voltage swell, MPPT-FM is able to result average THD on load bus of 1.594% smaller than MPPT-FS of 2.087%. The average THD of load voltage using MPPT-FM/MPPT-FS in nine voltage swell disturbances scenario on source bus, is smaller than voltage sag and it has met limit of IEEE 519. This research is simulated using Matlab/Simulink environment.

246

Authors: Agus Qomaruddin Munir, Sri Hartati, Aina Musdholifah

The spread of dengue hemorrhagic fever (DHF) globally with a frequency level that tends to be high in the past 50 years raises a systematic idea of prevention. One of the efforts to prevent DHF is the need for early identification of areas that are potentially epidemic. Early identification is carried out by getting an overview of the incident one step ahead by data forecasting. The focus of the study was the development of area stratification algorithms as an early identification of DHF outbreak areas by using data forecasting methods with surveillance data variables. Surveillance data which became the references for system modeling were DHF case data, rainfall, humidity, air temperature, wind speed and Larva-free Number (ABJ) for the span of 2010-2016 in 17 districts in Sleman Regency, Special Region of Yogyakarta. There were four steps during the study, i.e., 1) Forecasting of DHF case for the period of 12 months, 2) Forecasting of Larva-free Number (ABJ), 3) Determination of DHF case pattern for the last three years and the average of ABJ in the third year and 4) Area classification into stratification class. A method used for data forecasting of DHF case was seasonal autoregressive moving average (SARIMA), and the determination of area class pattern was conducted by using a neural network, meanwhile to obtain area stratification class used rule-based approach referring to guidelines controlling DHF outbreaks of the Ministry of Health of the Republic of Indonesia. Early identification was carried out by dividing into 4 area classes. Area class target included endemic (K1), sporadic (K2), a potential (K3) and free (K4). The testing of accuracy forecasting used relative mean absolute error (RMAE) for 12 months period. The results of the forecasting accuracy test on 17 districts in Sleman Regency showed RMAE average of 1.46 was considered low for it was still below 10%. Furthermore, the results of the early identification of area stratification classes in 2014 and 2015 from 17 districts showed that 3 of the four regions were endemic areas while in 2016 almost all districts were endemic areas and only one area was classified as sporadic.

270

Authors: Smita Avinash Lonkar, Kuraparthi Tirumala Viswanatha Reddy

Voice over Internet protocol (VoIP) is one of the online streaming method to provide voice communication that is used in the organization voice operation or conference call. In recent years, researcher developed several methods for improving the voice quality; but it still faces some problems of high jitter, delay and low throughput. In this paper, a VoIP is introduced to enable voice calls on Long Term Evolution (LTE). The Enhanced Voice Service (EVS) - Transmission Control Protocol (TCP) Reno-VoIP (EVS-TCP Reno-VoIP) method is introduced for enhancing the voice quality on LTE network. The EVS codec is used for improving the voice service and TCP Reno is used to avoid voice congestion in the LTE network. The testbed experiments on voice based skype application is implemented for LTE network. The network traffic is computed using the Jperf software. The Quality of Service (QoS) of the VoIP over LTE is analysed in Network Simulator 2 (NS2). The multiple scenarios were simulated to compute and analyse the performances in terms of an end to end delay, throughput, jitter and packet loss. The simulation results showed that EVS-TCP Reno-VoIP method has improved 2-3.5% of network performance compared to existing methods such as EVS codec, G-11, G-723 and SDSS.

287

Authors: Nam-Soo Kim

In this paper, we study a cooperative non-orthogonal multiple access (NOMA) system underlying cognitive networks which adapt user relaying and selection combining. The selected relay among candidate near users is applied max relay selection rule utilizing partial channel state information (CSI). The outage probabilities of the user relay, far user, and the system are derived in closed-form, and conformed through Monte Carlo simulations. Numerical examples show that the outage probability of the user relay decreases with the tolerable interference power increases under the given conditions. Also, the outage probability of the far user decreases as the number of candidate relays increase. However, we noticed that when the tolerable interference-to-noise ratio exceeds 30 dB under given conditions, further performance improvements of the user relay and the far user does not achieve. Regarding the proposed system performance, it is noticed that the optimal power allocation coefficient, which minimize the outage probability, of the far user is approaching to 0.86 as the number of the candidate relays increase.

305

Authors: Sharath Chandra Inguva, Joseph Beatrice Seventiline

The coordinate rotation digital computer (CORDIC) is a class of shift-add algorithms for rotating vectors in plane. Several techniques use the trigonometry function to compute the digital waves, but that requires expensive memory usage. Due to the flexible characteristics, CORDIC is best alternative and allows high quantization accuracy by maximum word length. The linear-rate convergence creates the major problem in CORDIC algorithm with the source of word-length and iteration speed. The power consumption also a major issue here to affects the performance by array of shift-add operations. For further enhancement, in this paper, we propose a low power and high speed CORDIC (LH-CORDIC) design with an improved power control and hardware reduction techniques. We employ the canonical signed-digit (CSD) technique and Hcub algorithm for reducing the number of shifters and adder/subtractor in the design. Then, we propose an adder based on the advanced Boolean logic technique. These three techniques are used to redesign the entire CORDIC logic stages thereby contributing in power consumption reduction. The functionality of proposed LH-CORDIC algorithm is assessed through FPGA implementations. The simulation result shows that the proposed method has higher frequency of 78.91%, 83.42%, 79.89% and 77.01% when compared with conventional CORDIC method.

10

Authors: Kolli Vasavi, Yaratha Madhavee Latha

In recent days the requirements of Biometric Identification System (BIS) increased enormously. BIS Uni-modal Biometric systems (UM-BS) have different kinds of problems like non-universality, noisy data, unacceptable error rate and spoof attacks. These limitations are solved by using multi-modal Biometric systems (MM-BS).MM-BS uses two or more individual modalities, like face, Palm, iris, retina, fingerprint, etc. This paper has introduced feature-level fusion and Rivest Shamir Adleman (RSA) encryption based FEP-RSA-MM biometrics system. This FEP-RSA-MM system has taken combination of Face, iris and Palm biological characters for individual Identification. FEP-RSA-MM was implemented by using MATLAB and the performance were calculated and assessed in terms of Recall, Sensitivity, Specificity, Accuracy, F-Score, Precision, Mean Square Error, Root Mean Square (RMS) Error, etc. The performance of this FEP-RSA-MM system mainly depends on the accuracy. The accuracy of FEP-RSA-MM system is 93.33 % and it improved compared to two existing methods GF-FLF-MM, SIFT-KNN-MM, FLF-GSO-MM and SLF-PSO-MM.

30

Authors: Kongara Deepika, Nallamothu Sathyanarayana

Now-a-days, research in educational mining focuses on modelling student’s performance. Many universities include large volumes of data related to student’s details, performance, management details, educational process, and etc. Moreover, most of the data remains unused because inability of the university administration to handle it, also huge volumes of data are difficult to perform. In this paper, hybrid Feature Selection (FS) method namely Relief-F and Budget Tree-Random Forest (RFBT-RF) is proposed for selecting active features to reduce high dimensionality and handle uncertainty of data. The proposed feature selection method selects only relevant features instead of selecting redundant and irrelevant features for the classifiers. Also, RFBT-RF method is applied on multiple classifiers like Decision Tree (DT), Naive Bayes (NB) and Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) for predicting the Student Academic Performance (SAP). RFBT-RF method was applied on three databases such as UCI (maths), UCI (Portuguese) and Collected dataset. Results showed that, RFBT-RF algorithm achieved 6.85% of improved SAP accuracy compare to the existing Logistic Regression (LR) model.

52

Authors: Yogesh Gandasi Suresh, Sharanabasaveshwar Gangadharayya Hiremath

The De-blocking filter is used in H.264 video codec for improving the quality of video. The De-blocking filter helps in removing the artifacts which are generated due to block based transform. In this research work, the Modified Filter Order (MFO)-Carry Select Adder (CSLA) based De-Blocking Filter (DBF) is introduced to improve the video quality, which is named as MFO-CSLA-DBF architecture. Conventional H.264 standard DBF requires more area due to a separate storage block of the horizontal and vertical data. To solve this problem, this paper implemented modified filter order architecture to reduce the memory and area for storing filter block data. The complexity of the boundary block has reduced by minimizing the boundary strength values. Furthermore, the CSLA design used for the data merging process in the DBF architecture instead of normal adder designs that also occupies less area in the MFO. The MFO-CSLA-DBF architecture improved the quality of the video by increasing Peak-to-Noise Ratio (PSNR), Structure similarity Index Matrix (SSIM) for different resolutions such as Quarter Common Intermediate Format (QCIF-176×144) and Common Intermediate Format (CIF-352×288). This MFO-CSLA-DBF architecture was implemented in the Xilinx tool by using the Verilog code for the Virtex-6 FPGA device. In this research work experimental results showed that MFO_CSLA_DBF architecture has reduced hardware utilization of FPGA for DBF from 2-3 % compared to the traditional DBF technique.

74

Authors: Hamela Kanagasundaram, Kathirvel Ayyaswamy

The routing protocol design with security and energy efficiency is a challenging task in mobile ad hoc network (MANET). To overcome this challenge, we propose an energy-efficient secured routing protocol. The objective of our work is to provide a secured routing protocol, which is energy efficient. To provide security the proposed technique allows each communicating node to monitor reputation factor, suspicion factor and contingency of threat to compute the degree of its vulnerability. The energy efficiency in optimized link state routing (OLSR) routing protocol is increased by generating new parameters and considering the energy of nodes. Based on the energy efficiency and security parameters the optimal route (multipoint relay) was selected. This approach uses less energy and secured compared to other energy models. The simulation result exhibits that the proposed multi objective Ant Lion Optimizer (MOALO) outperforms other state of art with respect to the performance metrics like energy consumption, Delay, throughput and network lifetime.

94

Authors: Moulay Seddiq El Kasmi Alaoui, Said Nouh, Abdelaziz Marzak

Digital transmission systems carry information from the source to the receiver using a physical medium such as cable, fiber optic or even propagation on a radio channel which isn’t entirely reliable and causes the change of data originally emitted. Today the use of error correcting codes for protection and correction becomes an integral part in the design of communication systems and computers. In this work, we present a new interesting way to accelerate the decoding process of linear codes. The proposed method called Soft Decision Decoder by Hash Techniques (SDHT) is based on syndrome-decoding algorithm and hash techniques. The use of this latest allows reducing considerably the search time of all possible error patterns of weights less than a fixed threshold. SDHT is applicable on many linear codes and exploit the polynomial form to reduce again the run time decoding for cyclic codes. SDHT is successfully applied to decode some Bose Ray-Chaudhuri and Hocquenghem (BCH), Quadratic Residue (QR) and Extended Quadratic Residue (EQR) codes. The simulation results show that the proposed SDHT yield to good error correcting performances with reduced complexity. The comparison between SDHT and many competitors shows that it gives better performances in terms of correction rate. The experimental study of the decoding steps for the BCH(63,45,7) code shows that the time search of the most likely error pattern is reduced at about 26214153% comparing to an exhaustive search of all possible error patterns of weights less than or equal to 4. This study proves the huge success of the proposed SDHT decoder.

114

Authors: Pradeep Krishnadoss, Prem Jacob

Cloud computing provides increased performance, scalability and is both cost efficient as well as low accounts for maintenance, which makes it a preferred choice when the dynamic allocation of resources is considered. Of the various advantages that cloud computing provides, task scheduling is an essential feature that helps to boost performance and reduce operation cost. In the proposed OLOA, a solution is provided for optimization, taking the makespan and cost as major constraints. This is accomplished using the two algorithms, Lion optimization algorithm (LOA) and the Opposition Based Learning (OBL) algorithm; and creating a hybrid Oppositional Lion optimization algorithm (OLOA). The given solution is simulated and demonstrated in the cloudsim programming environment, where the obtained results show drastic improvement in performance, in comparison to that of the previously used other existing algorithms such as Particle Swarm Optimization (PSO) algorithm, oppositional learning based grey wolf optimizer (OGWO) and the Genetic algorithm (GA), all of which do not match the performance rates of the proposed hybrid algorithm.

133

Authors: Gowtham Nagaraju, Shobha Shankar

In this paper, gravitational search algorithm (GSA) is used for improving the control strategy of unified power quality conditioner (UPQC). The UPQC is considered with wind energy conversion system (WECS) and grid/load to determine the dynamic behaviors. The proposed control technique alleviates the power quality (PQ) problems in the WECS. Here, the GSA approach is used to identify the ideal solutions from the available search space and the method enables the UPQC to perform under different test conditions. Initially multiple parameters are considered such as, voltage, real and reactive power and current. Amid the load variations, the proposed method will regulate the power loss, and voltage instability problem. The proposed system is tested for different PQ issues. In order to evaluate the effectiveness of the proposed method, three different cases are considered they are performance of GSA under balanced condition, under unbalanced condition, under motor speed condition. The performance of the proposed GSA based UPQC system is validated through simulations using MATLAB/Simulink and compared with existing techniques such as FA, base model and ANFIS techniques. From the comparison analysis, we can infer that the proposed control technique is very much effective in enhancing the power quality of the system than the existing techniques.

151

Authors: Mariya Ouaissa, Abdallah Rhattoy

The new Internet of Things (IoT) applications are enabling Smart City initiatives worldwide. It provides the ability to remotely monitor, manage and control devices. The main features of a smart city include privacy of users and security of communication. The distributed architecture of IoT aims at providing location awareness and low-latency interactions to Machine-to-Machine (M2M) applications. In this context, the Long Term Evolution (LTE) technology and its evolutions are expected to play a major role as a communication infrastructure that guarantees low deployment costs and embedded security, we show how a network deployment that exploits Device-to-Device (D2D) communications and Wireless Sensor Network (WSN), currently under definition within 3rd Generation Partnership Project (3GPP) and non-3GPP access network, can be employed to support efficient communication between M2M devices (nodes and smart objects) and the core network in LTE system. In this paper, we propose a secure group model include different authentication mechanisms combined between D2D and WSN technologies that allow the protection of M2M devices through strong encryption and authentication means, so that devices can benefit from high security functionalities without however having to execute computationally intensive operations with a low communication complexity. The results obtained show that our model firstly solve the initial key establishment and integrity problems in the presence of the inside adversaries in networks and offers best performance than the standard Bluetooth in D2D link. Secondly our proposed examine the area of authentication for sensor networks by make a combination between algorithms such as TinyPK and Timed Efficient Stream Loss-Tolerant Authentication (TESLA) in order to secure the link between sensors and achieve a high levels of security.

174

Authors: Geetha Megharaj, Mohan Kabadi

One of the most pressing and critical issues in cloud is Power Optimization. Due to the popularity of cloud, many computing applications are being hosted in the cloud. Naturally, many Cloud Centers (CCs) are experiencing huge power consumption problem, which leads to higher operational cost and environmental hazards due to carbon emissions. The two important concepts used to achieve power optimization are: load balancing and server consolidation. The first concept aims to achieve fair distribution of computing load on different Physical Machine (PM); whereas, the second concept aims to shutdown PMs, which are having limited computational load. One of the most popular techniques to achieve load balancing and server consolidation is Virtual Machine (VM) migration technique; where, the VMs are packed inside limited PMs; such that, load is fairly balanced among PMs, and limited usage PMs are relieved of their computational load and can be shutdown. However, VM migration requires excessive cost, and results in excessive computed task wastage. Hence, in this work, a new hybrid scheme which does not migrate VMs, and migrates only suitable tasks from overloaded VMs and lightly loaded PMs is presented. The proposed hybrid model is compared against contemporary VM migration technique to assess resource usage merits. The proposed hybrid model significantly outperforms the contemporary VM migration techniques in resource usage efficiency.

201

Authors: Udoum Chhor, Uthen Leeton, Keerati Chayakulkheeree

In this paper, a probabilistic optimal power dispatch (POPD) using linear programming (LP) is proposed for solving the power generation dispatch with price-based real-time demand response (PRDR). The expected short-term load forecast is represented by a probabilistic distribution function. The simulation result has prosperously shown that the proposed method could handle the POPD solutions for real power dispatch considering PRDR by using probabilistic Truncated normal distribution function (PTNF). The PTNF is used in a vigorous part of the framework to eject the infeasible results during the computation. This would lead the results to the high degree of accuracy comparing to the normal sampling methods. The simulation results showed that the proposed method can efficiently and effectively minimize the total power generation cost while trading off the PRDR cost in the POPD problem with load uncertainty.

221

Authors: Ramkumar Jaganathan, Vadivel Ramasamy

The rapid advancement of Cognitive Radio Ad Hoc Network (CRAHN) has created a demand for effectively utilizing the spectrum without affecting the environment. End-to-end delay in CRAHN affects the performance in numerous ways. The major task of routing protocols is to transfer the data to the destination in a shorter duration by utilizing low energy. Congestion in the network leads a way to end-to-end delay and exhaustive energy consumption, but optimization can be used to control it. This research work aims to propose a bio-inspired routing protocol inspired from characteristics from wolves to reduce the overall end-to-end delay which results in reducing the consumption of energy and extends the lifetime of the network. This research work utilizes M/G/1 queueing model to avoid network congestion, where the requests from nodes are markovian and service times regarding the requests have general distribution time. With benchmark performance metrics NS2 simulations are carried out to check the performance of the proposed protocols. Results conclude that the proposed protocol significantly outperforms than other protocols with better packet delivery ratio, reduced end-to-end latency and energy consumption.

242

Authors: Youness Khourdifi, Mohamed Bahaj

The prediction of heart disease is one of the areas where machine learning can be implemented. Optimization algorithms have the advantage of dealing with complex non-linear problems with a good flexibility and adaptability. In this paper, we exploited the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant features in order to improve the quality of heart disease classification. Then, we perform a classification based on different classification algorithms such as K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Random Forest and a Multilayer Perception | Artificial Neural Network optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. The proposed mixed approach is applied to heart disease dataset; the results demonstrate the efficacy and robustness of the proposed hybrid method in processing various types of data for heart disease classification. Therefore, this study examines the different machine learning algorithms and compares the results using different performance measures, i.e. accuracy, precision, recall, f1-score, etc. A maximum classification accuracy of 99.65% using the optimized model proposed by FCBF, PSO and ACO. The results show that the performance of the proposed system is superior to that of the classification technique presented above.

263

Authors: Jayavignesh Thyagarajan, Subashini Sundararajan

Multi-hop based Wireless sensor network (WSN) has gained significant importance catering to large scale application demands and integrated with Internet of Things (IoT) in recent times. Conventional WSN faces hotspot problem, since nodes in the one-hop neighbourhood of static sink faces battery depletion rapidly for relaying other nodes’ traffic. Mobile sink based approaches suffer from excessive control overhead in terms of topology updates, route computation and maintenance by sensor nodes. This work proposes a novel quasi-mobile sink query driven model to optimize cost factors such as deployment, energy and routing for large scale soft real-time applications. A corona based beacon-less flexible tree routing is adopted. In this model, sink moves in a predefined pattern covering the deployment zone but remains stationary for a configurable sojourn time during the query dissemination and data gathering phase. It is inferred from simulation results that significant energy savings is achieved over the hotspot region near the sink, with increase in traffic rate compared to static sink solution. High data delivery ratio and energy efficiency is achieved with increase in network size in comparison with existing sink mobility models. Empirical range test measurements of average Received Signal Strength Indicator (RSSI), Packet Success Rate (PSR) are also performed to determine the optimal transmission power level and ensure proper deployment for effective coverage and network connectivity.

290

Authors: Banka Jyothsna Rani, Ankireddipalli Srinivasula Reddy

In modern days, utility grids are facing major issues in the power system due to an uninterrupted load development and shortage of capital investment in transmission systems. Due to this, network is not capable to meet the estimated demand and transfer the produced power from the integrated power generation into the distribution network. This may generate high power losses in the distribution side that also disturbs the overall efficiency of the network. By employing an optimal placement of Distributed Generation (DG) in power systems is the only way to minimize this kind of problems in economical and practical way. DG has several benefits over centralized power generations that comprising system steadiness, improvement and dismissing transmission/distribution system bottleneck to meet the power demand. So in this paper, Binary Particle Swarm Optimization (BPSO) is used for optimal DG reconfiguration. BPSO iteratively moves the DG in each load bus of the test system that provides the smallest value of the objective function, which is considered as the finest candidate for DG connection. This method is simulated on IEEE 69-bus system that showed enhancement in voltage profile and decrease in power loss compared to other optimization methods.

310

Authors: Prasad Rayi, Makkapati Venkata Siva Prasad

Massive Multi-Input and Multi-Output (MIMO) antenna system is considered as the key technology to improve both spectral efficiency (SE) and energy efficiency (EE) for 5G wireless systems. This paper evaluates the impact of perfect and imperfect channel state information (CSI) on the SE. The maximum SE of 200 bits/s/Hz achieved in a multi-cell scenario and its limiting factors were explored. We mainly demonstrated linear precoding algorithms such as zero-forcing (ZF), maximum ratio combing (MRC), and minimum mean square error (MMSE) for SE analysis. The explicit SE and EE expressions were derived from Shannon capacity calculations for single and multi-cell scenario. The proposed ZF and MRC precoding schemes were observed with SEs of 185 bit/s/Hz, and 180 bits/s/Hz respectively with pilot reuse factor with λ=7. The simulation results were validated by comparing the SE versus the number of base station (BS) antennas with receiving schemes by using MATLAB 2018a.

11

Authors: Boonlert Suechoey, Chongrak Boonseng, Chow Chompooinwai, Chai Chompoo-inwai

Under renewable generations mixed in today’s modern grid system, many of renewable generation resources tend to increase the fault level to an existing power system. Rules and regulation for power equipment and devices need to be revised and updated, of course, no exception for the distribution transformers. This article, therefore, presents the evaluation and analysis of the winding temperature on both loading and short circuit conditions of an oil-immersed distribution transformer and propose the novel transformer design method for improving short circuit withstand capability. In the study, the methodologies of measuring and estimating the winding temperature of the transformer during short circuited are presented and implemented. In the study, various winding parameters are analysed including: the winding temperature, the hottest temperature of the winding after a short circuit, the short circuit current, short circuit force and short circuit duration. The tested and analysed result are benefit for the newly proposed distribution transformer design of a 400 kVA 3 phases 50 Hz 22 kV-400/230 V, Dyn11. The new design approaches will enable designers to find a weak spot and proper selection of raw materials, such as winding size, insulation thickness and properties of the silicon steel for a better quality of distribution transformer. Moreover, the new design can offer lower winding temperature rise of transformer while loading or experiencing with short circuit conditions meaning that it can prolong transformer insulation and extend transformer lifetime.

33

Authors: Jayanthi Chandrashekar, Arun Manoharan

Providing a reliable and secure communication in Mobile Adhoc Network (MANET) is a highly challenging and demanding task in recent days. For this purpose, different techniques are developed in the traditional works, but it lacks with the drawbacks of difficult key revocation, reduced security, increased delay, and bandwidth usage. To solve these problems, this paper motives to develop a routing technique, namely, Identity Based Key Management (IBKM) for providing a secure communication in MANET. Here, the group polynomial equation is generated at first for generating a unique ID to each node in the network. Then, the neighboring nodes of the source and destination are identified and registered in the network table. After that, the routing path between these nodes are validated in order to enable a reliable communication. The node pairing is performed and the public key is distributed to the nodes that are successfully paired. Sequentially, the signature of the packet is generated and verified, if both generated and received signature are same, the packet is received; otherwise, the misbehavior node is identified and blocked. To evaluate the results of the proposed mechanism, various performance measures such as malicious node detection probability, average message overhead, Packet Delivery Ratio (PDR), end-to-end delay, throughput, and routing overhead are used. Here, the results are evaluated with respect to various malicious node ratio, speed (m/s), and simulation time (s). Also, the existing Dynamic Source Routing (DSR) and Cooperative Bait Detection Scheme (CBDS) are compared with the proposed mechanism for proving the efficiency. From the results, it is analyzed that the PDR and throughput of the network is increased with the reduced message overhead and delay. Also, the proposed mechanism uses the identity based key management technique, which efficiently detects the malicious nodes in the network.

52

Authors: Sunil Swamilingappa Harakannanavar, Prashanth Chikkanayakanahalli Renukamurthy, Kori Basava Raja

Personal identification based on face recognition is receiving extensive attention over the last few years in both research and real-time applications due to increasing emphasis on security. In this paper, Face Recognition using the Fusion of bit-plane and binary image compression techniques is presented. Face images are resized to 256×256 to obtain the uniformity in the size of face images. The Bit-Plane compression algorithm is applied on the resized image to extract the features of face. Similarly, the Binary image compression technique is applied on 256×256 resized images to extract the features. The features produced from both the compression techniques are added to obtain the final facial features. Finally, the test features and database features are compared using Euclidean Distance classifier. Extensive experiments are conducted on combined, Indian female, Indian male and JAFFE face database. The true success rate of the proposed model provides better recognition rate than other existing state-of-the-art methods.

75

Authors: Raja Murali Prasad, Gandluru Arthur Edwin Satish Kumar

As cluster heads chosen by nodes, gathers the data from their individual groups and exchanges the collected data to the mobile sink as proposed in the heuristic tour planning technique. In this research, the mobile sink starts the collection of data irregularly from the initial site, which is stored in cluster heads in an individual hop extend and at last returns to the initial site. In existing approaches, scheduling time for data delivery is less. To increase the data delivery, there is a need in extending the concept to higher level routing process. To solve this issue, this paper proposed a collection oriented distribution scheme with k-medoids (CODS-KM). The method can be exhausted for distribute the collection points in network in a particular manner. In this method, k-medoids algorithm for clustering and cluster head process is used. To collect the data purpose, it can be used CH (Cluster Head), CP (Collection Point) and RP (Rendezvous Point). The proposed system helps in mitigation of travelling time when compared to existing approaches. To find the effectiveness of proposed algorithm, NS2 (Network simulator-2) software is used for simulation verification.

95

Authors: Ngoc Thuy Pham, Diep Phu Nguyen, Khuong Huu Nguyen, Nho Van Nguyen

In this paper, a novel Stator Current Model Reference Adaptive System (SC_MRAS) based scheme using Neural Network (NN) for sensorless controlled of Six Phase Induction Motor (SPIM) drive is presented. For this scheme, the measured stator current components are used as the reference model to avoid the pure integrator problems and reduce influence of motor parameter variation. The two layer linear NN stator current observer is used as an adaptive model, which has been trained online by means of a Back Propagation Network (BPN) algorithm, to estimate rotor speed and update stator resistance value. The Voltage Model (VM) based rotor flux identifier with value of stator resistance is updated online is used to provide the rotor flux for the adaptive model, this helps to overcome the instability problem in the regenerating mode of operation that occur when using current model for rotor flux identifier and enhance the low speed operation performance of the proposed scheme. Simulation results have demonstrated that the proposed MRAS observer is significantly improved especially at low and near zero speed range, Sensorless results also confirm the instability problem not exist in the regenerating mode in low speed region.

118

Authors: Tulasi Sunitha Manepalli, Chamakuzhi Subramanian

Rainfall is considered as the most important phenomena of the climate system. Due to the lack of adequate irrigation facilities, agriculture becomes vulnerable, which is the backbone of a country's economy. The rainfall can be able to predict by using selective appropriate predictors. Though several models have been developed for forecasting and predicting in Time Series (TS), there is no ideal model to predict the rainfall. In recent years, Automata is useful for forecasting and prediction of hydrological TS because automata help to predict the rainfall from the uncertainty data. The motivation of this work is to design a reliable tool for predicting daily rainfall in advance using Regression Automata (RA) models. The proposed method uses three different RA models for predicting rainfall from the collected data for four stations in Queensland State. The results clearly show that the all the three RA models can predict the rainfall very efficiently in various terms such as error rate, coefficients and mean square error.

137

Authors: Kishore Verma Samraj, Rajesh Appusamy, Ramya Ravi Shankar

Increase in the use of digital platforms generate a very huge amount of user data which on processing reveals more valuable information about users while mining or it may also reveal some future events. Privacy preserving data mining (PPDM) is the current developing area of research that precisely ensures a certain level of privacy by increasing the utility of information. Data Anonymization is a PPDM technique that protects sensitive information in the recordset with high confidence. Anonymizing recordsets that consists of null values habitually referred as deficient recordsets, suffer from serious information loss owing to null value contamination. This would be arisen because of the null values present in the original recordset. In this paper, we proposed an enhanced ARX framework to anonymize the deficient recordsets through a higher level of privacy and utility. We explore the characteristics of null value contamination on generalization, driven by these characteristics, we formulate an Enhanced ARX Anonymization- (K,R_n^N )Anonymity Model to preserve data utility on deficient recordsets. The results obtained by executing our framework over the real - world dataset proved to be optimized in providing better trade-off between utility and privacy for deficient recordsets in Cell oriented Anonymization (CoA), Attribute oriented Anonymization (AoA) and Record oriented Anonymization (RoA) than the existing procedures.

156

Authors: Vinay Balasubramanyam, Kalappan Balachander

Electromyography (EMG) signal is one of the important tools for the detection of skeletal related muscular information. In this scenario, we analyse the normality and abnormality of knee movements for pre-predicting the knee dystonia diseases. It is characterized by involuntary sustained muscle contractions affecting one or more sides of the body, frequently causing twisting and repetitive movements or abnormal postures. In this paper, EMG based knee dystonia pre-prediction is assessed by employing EMG-Lower Limb dataset. Then, transformation is done by using discrete wavelet Transformation (DWT), which delivers a superior time and frequency localization ability. After transformation, hybrid feature extraction is performed by employing yule-walker, burg’s, voltage level (minimum and maximum), Renyi entropy, and Peak-Magnitude to Root Mean Square Ratio (PMRS) for achieving optimal feature subsets and also to reject the redundant and irrelevant features. In most of the existing studies, an individual feature or two different features are combined for extracting the feature values from the acquired signal. In this research, five effective entropy and autoregressive features are combined to obtain more active features. After obtaining the active features, a superior binary classifier: Kernel Nearest Neighbour (KNN) is implemented for classifying the normality and abnormality of knee movements. The experimental outcome proves that the proposed methodology effectively distinguishes the normal and abnormal knee movement in terms of sensitivity, specificity, recall, precision, accuracy and E-rate. The proposed methodology improves the classification accuracy in knee movement detection upto 0.785-1.65% compared to the existing methods.

177

Authors: Deepa Nataraj, Rajaji Loganathan, Moorthy Veerasamy, Viswanatharao Jawalkar

A novel metaheuristic optimization procedure for optimizing electrical distribution system is proposed that may help the system planners to know the minimum and maximum objective values. In which the optimal distribution network reconfiguration (DNR) framework and implementation of modified grey wolf optimization (mGWO) algorithm to minimize power loss reduction and node voltage deviation indices have delineated. The DNR involves nonlinear and multimodal function that has been optimized under practical constraints. For the purpose, mGWO algorithm is employed for ascertaining optimum switching position while reconfiguring the distribution system at a minimum fitness value. In fact grey wolf’s update its position linearly from a higher value to zero in the search vicinity that provides perfect balance among intensification and diversification to ascertain the best fitness function and exhibits quick and steady convergence. Moreover, the proposed method seems to be a promising optimization tool for the electrical utilities, thereby modifying their operating strategy of distribution system under steady state conditions. This study is conducted on standard IEEE 33-bus and 69-bus distribution systems, the simulated results have analyzed and compared with several recent methods. It shows that the numerical results have provided, by mGWO is superior among the contestant algorithms.

199

Authors: Medi Sandhya Rani, Redamalla Rekha, Kota Venkata Naga Sunitha

Mobile Ad-Hoc Network (MANET) is a self-configuring network of mobile nodes associated by wireless channel. The nodes are free to move arbitrarily anywhere in the network. Hence, lack of fixed infrastructure and host flexibility, providing secure communications is a big challenge. Because a temporary device recurrently joins or leaves a network, the authentication and security techniques should be equipped for the unorganised users. In cryptography it is ineffective if its key management is fragile. Key management is one of the vital aspects for security. Thus, the network's wireless topology may be disorganised and may adjust rapidly, the efficient-route is recognised using Multi-path AODV Routing-Protocol. The ID based SKMS (Secret Key Management Scheme) method is implemented in secured environment by using Rivest-Shamir Adleman (RSA) method and the Node ID based Secure Key Management Scheme (SKMS) mechanism for securing data packets from source to destination. The ID based is used to share secret key of an individual users. A multi-cast (AODV) group receiver or group-source-key pair is conserved and recognised by an ID. Each clustering group has its own exclusive identity number. “ID based Multicast SKMS using RSA” method increase in packet delivery ratio (6%) along with the decrease in drop (5%), delay (8%), and energy consumption (6%) in secured ad-hoc environment.

220

Authors: Saïd Nouh, Moulay Seddiq El Kasmi Alaoui, Mostafa Belkasmi, Abdelaziz Marzak

Quadratic Residue codes are among the best codes. They have high capacity of error correction but they are very difficult to enumerate and therefore to analyse. Despite all developed methods in this domain, the weights enumerators of Quadratic Residue codes are known only for lengths less than or equal to 167. For the lengths 191 and 199 only estimations are available. In this paper, we present a new method based on the Multiple Impulse Method (MIM) and hash techniques to find the weights enumerators of Quadratic Residue codes having lengths in the form 8m-1, for an integer m. The proposed method Hash_MIM_Weights_Enumerators is validated on all Quadratic Residue codes of known weights enumerators; its reduced spatial and temporal complexities yields to new important results. So, the weights enumerators for the lengths 191, 199 and 223 are determined. These three codes are the best binary linear block codes in terms of minimum distance known until today and their analytical performances are remained unknowns in more than 60 years ago and they are available now.

244

Authors: Alugoju Sreelatha, Chagunta Venkata Guru Rao, Porika Sammulal

Cross Media Retrieval (CMR) is one of the emerging research areas in the field of internet services and multimedia technology. The primary objective of this paper is to develop an effective dictionary learning methodology to reduce the semantic gap between the low-level and high-level features. In this experimental research, the image and text data in the Wikipedia dataset are separated, after separating the image and text data, feature extraction is performed individually. A suitable combination of feature extraction methods (normalized histogram colour feature and bag-of-words) are under-taken for image and text feature extraction. The normalized histogram colour feature has very quick generation, compared to other feature vectors. In addition, Bag-of-words measures the repetition of the words in the bag and retrieves the data similar to the query data. Also, it removes the conjunction words and the remaining words are named as keywords. These keywords are used as the query text and it compared to the proposed dictionary learning. Then, retrieves the text and image related to the keywords based on Minkowski distance measure. Finally, the experimental outcome shows that the proposed approach delivers better performance in terms of Mean Average Precision (MAP) value, retrieval efficiency, precision and recall. The proposed methodology improved the MAP value up to 0.24-0.20 compared to the existing methods.

261

Authors: Adna Sento, Yuttana Kitjaidure

This paper presents a new model of the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm, called the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm (NNPID-AC). The proposed NNPID-AC controller is designed to develop the performances and the speed of calculation under the iterative learning algorithm. In the learning algorithm, the critic algorithm receives the reward value and control input to criticize the current state using the action-state value function approximation. Furthermore, instead of applying every available action to predict the local successor state, the algorithm only uses one-step estimation using the fifth degree spherical-radial cubature rule algorithm. To evaluate the proposed NNPID-AC controller, the robot arm MATLAB simulations have been implemented and provide the control system with the load and noise to prove the robustness and fault tolerance, respectively. From the results, the robot arm control system simulation under the control of the proposed NNPID-AC controller can potentially track the error and gives the best responses compared with the other conventional controller either with or without the load and the noise disturbance.

282

Authors: Sindia Thuraipandian Vimala, Julia Punitha Malar Dhas

The usage of cloud computing is skyrocketing now-a-days and so is the network traffic. The adversaries intend to attack the cloud servers due to some intentional reasons. The most frequent attack is the data theft, which is followed by the DDoS attack. Though numerous solutions exist to handle DDoS attack, SDN based solutions for cloud computing is scarce. Understanding the need of the DDoS attack detection system, this work proposes a SDN based solution for detecting DDoS attacks in cloud computing environment, which relies on ensemble classifier. This work collects the real time traffic data with the help of Wireshark network analyser tool. The ensemble classification relies on the classifiers k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The performance of the proposed approach is analysed in terms of accuracy, sensitivity, specificity and the results are compared with the existing approaches. Additionally, in order to prove the potentiality of the ensemble classification, this work employs the classifier individually and the results are compared. Finally, the average attack detection time is measured and compared. From the experimental results, it is observed that the proposed approach proves better results in terms of accuracy, sensitivity and specificity.

300

Authors: Abdelali Zakrani, Mustapha Hain, Abdelwahed Namir

There is evidence that Software Development Effort Estimation (SDEE) plays a crucial role in managing the software project and controlling its whole lifecycle; an accurate effort estimate allows an effective monitoring and efficient scheduling of tasks and resources. Although extensive research has been carried out on SDEE techniques, no single technique has been shown to be superior to other in all situation. Recently, there has been an increasing amount of literature on predicting software effort using Machine Learning (ML) methods. Among these ML techniques, regression tree-based models have gained a considerable attention due to their generalization ability and understandability. So far, very few studies have investigated the potential of Random Forests (RF) in software effort estimation. In this paper, a RF model is designed and adjusted empirically by varying the values of its key parameters. Prior to the parameters adjustment, we analysed their impact on RF model accuracy which allows an efficient tuning of the model during the training stage. The performance of the RF is then evaluated and compared with that of classical Regression Trees (RT). The evaluation was performed through the 30% hold-out validation method using five datasets: ISBSG R8, Tukutuku, COCOMO, Desharnais and Albrecht. To identify the most accurate technique, we employed three widely known accuracy measures: Pred(0.25), MMRE and MdMRE. The results obtained show that the adjusted random forest outperforms the regression trees model on all evaluation criteria. Moreover, the proposed model performs better than some recent techniques reported in the literature for software effort estimation.