International Journal of Knowledge Engineering and Data Mining (8 papers in press)
CLUSTERING ANALYSIS ON THE DESIGN OF COMPLEX DNA NANOSTRUCTURES USING DNA HYBRIDIZATION, SWARM INTELLIGENCE AND GENETIC ALGORITHM
by Ganeshbabu Karmegam
Abstract: The main objective of this work 'clustering analysis on the design of complex DNA nanostructures using DNA hybridisation, swarm intelligence and genetic algorithm', is to study the clustering techniques with swarm intelligence and genetic algorithm to create new algorithms and to improve the existing algorithms in order to apply them to the issues of design the nanostructure to computationally simulate the DNA sequence recognition process and to achieve an optimised conformation for the complex structure, the relative orientation between motif pattern and DNA hybridisation such that to bind the complementary DNA sequences to form structure. In the design of nanostructure, DNA molecular recognition capacities make DNA an appealing candidate for the construction of novel nanomaterials. The modified Boyer Moore algorithm and modified K-nearest neighbour is for design the complex nanostructures to improve a candidate solution with regard to a given measure of quality.
Keywords: Clustering; Swarm Intelligence; Genetic Algorithm; DNA Nanostructures; DNA Hybridization.
Performance improvement options of scientific applications on XeonPhi KNL architectures
by Shajulin Benedict
Abstract: Intel's recent manycore processor KNights Landing (KNL) promises high performance for scientific applications. Careful tuning for the complex chip architecture is required to efficiently exploit the chip's hardware resources. This paper describes performance improvement techniques and demonstrates their effectiveness for scientific applications. Experiments were conducted with some of the National Aeronautics and Space Administration (NASA's) advanced supercomputing (NAS) parallel benchmarks, and the effectiveness of: 1) advanced vector extensions (AVX-512) vectorisation support; 2) manycore threading support; 3) the utilisation of thread affinities for different KNL modes, was analysed.
Keywords: knights landing; performance analysis; performance tuning; scientific applications.
An analysis of the 2016 US presidential election using Chanakya - a knowledge discovery platform for text mining
by Rashmi Malhotra, Kunal Malhotra
Abstract: In this era of information overload, discovering knowledge is a challenge. However, a new generation of text mining tools enables researchers and practitioners to analyse large volumes of data. This paper illustrates the design of knowledge discovery system - Chanakya using text mining. Chanakya works in two stages. Stage 1 uses naive Bayes classifier, a supervised machine-learning algorithm to train for classes, as we explicitly provide training data that is labelled with classes. Stage 2 uses k-means analysis, an unsupervised machine-learning algorithm to determine what categories are emerging from the mentions of each class. We use the 2016 presidential elections Twitter feeds to illustrate the use of Chanakya. Chanakya offers a commentary on the current state of the political arena after analysing the candidate tweets and how people are reacting to these tweets.
Keywords: text mining; k-means analysis; supervised machine learning; Bayes classifier; knowledge discovery; USA.
Towards a scalability approach for risk mitigation in electrical systems
by El Yasmine Ait Si Larbi, Ghalem Belalem, Bouziane Beldjilali
Abstract: Electrical energy has been used for decades to power domestic equipment, small and large industries and transportation with the electrification of everything, the availability of electricity becomes crucial. In order to guarantee the continuity of the electrical energy supply, companies must have effective means to prevent risks that may arise on the electricity networks. This paper is focused on the risk of insufficient power supply for end customers. The solutions consist of upsizing the existing equipment configuration and/or add other equipments in order to guarantee an increase in power supply. The proposed approach is based on distributed artificial entities corresponding to the distributed enterprise actors. We propose a control system, based on a decision making system to help decision-makers to take the best scalability solution. The results show that the degree of effectiveness of each type of scalability depends on one electrical system configuration to another.
Keywords: decision support system; DSS; scalability; control system; electrical systems; risk management; risk mitigation; metrics; risk indicators; vertical scalability; horizontal scalability.
Image optimisation using dynamic load balancing
by Ganesh V. Patil, Santosh L. Deshpande
Abstract: Image processing with variable measured gadgets with various resolutions and display sizes is a basic errand. For variable display sized gadgets, maintaining the quality of image is the major challenging task. The present investigation test highlights dynamic load balancing-based way to deal with this issue. Dynamic load balancing is applied using both parallel and pipelined architectures based on runtime state information of underlying resources. In exhibit think, we have considered image processing task as both CPU and memory intensive task and the impact of architectural contrast is studied. The optimally-sized image for a targeted display-sized device with better resolution is provided using three staged either pipelined or parallel processing. The steps involved in processing are resizing, quantisation and compression. This technique likewise gives better resource and storage utilisation.
Keywords: Dynamic Load Balancing; Pipeline Architecture; Parallel Architecture; Image Optimization; Image Resizing; Image Quantization; Image Compression.
A novel channel state prediction technique for overlay cognitive radio-based emergency sensor networks
by Swagata Roy Chatterjee, Mohuya Chakraborty, Debashis Saha
Abstract: For overlay cognitive radio (CR), this paper proposes a knowledge-based spectrum prediction technique using artificial neural network. This is targeted for CR-based emergency sensor networks in real life applications, where extremely reliable data transmission is mandatory. The technique operates in two steps: 1) identification of idle states in primary user bands; 2) prediction of noise level of the idle bands. The goal is to select a less noisy vacant band among the detected idle bands. The system is analysed under both Gaussian mixer noise (GMN) distribution and additive white Gaussian noise (AWGN) by predicting the current state of the channel in low SNR environment. Simulation results using MATLAB R2015b show satisfactory values for probability of false alarm (0.17 and 0.3), and probability of misdetection (0.12 and 0.25) for AWGN and GMN, respectively.
Keywords: cognitive radio; emergency communication; overlay mode; artificial neural network; ANN; spectrum sensing.
Multi channel selection using distributed mutual exclusion and multi-criteria decision in cognitive radio networks
by Asma Amraoui
Abstract: The cognitive radio (CR) technology allows the secondary user (SU) to share the licensed spectrum by using negotiation or cooperation methods issued from dynamic spectrum access (DSA). In this research paper we propose a method based on multi-criteria decision to deal with many parameters and after that we propose an algorithm for distributed mutual exclusion (DME) to solve the problem of channel selection. The performance of the proposed solution is evaluated through extensive simulations and achieves satisfactory results in term of processing time, response time and especially the number of exchanged messages.
Keywords: cognitive radio; multi-agent systems; dynamic spectrum access; multi-criteria decision; distributed mutual exclusion.
Fuzzy logic-based single document summarisation with improved sentence scoring technique
by Darshna Patel, Hitesh Chhinkaniwala
Abstract: Text summarisation is compressed or condensed version of any text document. Due to increasing use of digitisation, massive amount of information is available on internet. Text summarisation is an emerging alternative for users to find relative information in automated shortened versions. In this paper we propose single document summarisation technique using shallow features of sentence to generate summary. The weight of sentences is calculated by applying score of different words and sentence-based statistical features. Here, most salient sentences are selected based on weight of sentences and are put together to generate summary. This is modeled using fuzzy inference system. This approach utilises fuzzy inference and fuzzy measures to find most significant sentences. The result of our proposed method is compared with other methods using recall oriented understudy for Gisting evaluation (ROUGE-N) measures on document understanding conferences (DUC) 2002 dataset and results show that our proposed method outperforms a few baseline methods.
Keywords: text mining; extractive summarisation; text summarisation; feature extraction; fuzzy logic; statistical features; rouge score; DUC data; sentence scoring.