Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool. With this motivation, this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics (MR-HDBCC). The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data. In addition, the MR-HDBCC technique involves three distinct processes namely pre-processing, clustering, and classification. The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique which is capable of detecting random shapes and diverse clusters with noisy data. For improving the performance of the DBSCAN technique, a hybrid model using cockroach swarm optimization (CSO) algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering. Finally, bidirectional gated recurrent neural network (BGRNN) is employed for the classification of big data. The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.     

Fun with photons: selective light induced reactions in solution and in water soluble nano-containers

Two distinct strategies for controlling selectivity, in particular stereoselectivity in photochemical reactions are reviewed. In the first strategy, supramolecular approach using cucurbituril nano-containers in catalytic amounts is employed to control selectivity during photochemical transformations. In the second approach, a generalized methodology for carrying out light-induced transformations in solution at ambient conditions is detailed where axially chiral motifs are employed to enantiospecifically transfer the axial chirality in the reactant to point chirality in the photoproduct(s).     

A novel layer 3 based movement detection algorithm for improving the performance of mobile IP

Mobile IP (MIP) provides efficient mobility support to applications at transport layer or above. MIP can provide better quality of service (QoS) in heterogeneous environment if it relies on layer 3 based movement detection techniques. Among the existing movement detection algorithms (MDAs), enhanced lazy cell switching (ELCS) provides better performance compared to eager cell switching (ErCS) and early cell switching (EyCS) by reducing movement detection delay when the mobile node (MN) crosses the overlapping cells. On the other hand, movement detection delay incurred by ErCS is always low compared to ELCS while the MN crosses adjacent cells. Thus, in this paper we propose a new MDA by retaining the positive features of ErCS and ELCS to provide better QoS in a network that comprises overlapping cells and adjacent cells. Our proposed MDA reduces the usage of radio resources of the MN and foreign agent (FA) in overlapping zone compared to ELCS. The simulation results on NS-2 demonstrates that our proposed MDA improves the performance of MIP as compared to other MDAs in terms of number of TCP data packets received by the MN, TCP throughput and amount of data transferred during simulation while MN crosses the overlapping area of two cells as well as when it roams in a network consisting of both types of cells overlapping and adjacent.     

Strength of cold-formed lipped channel beams under interaction of local,distortional and lateral torsional buckling

Cold-formed thin-walled lipped channel steel beams may undergo buckling modes such as short half-wavelength local buckling, intermediate half-wavelength distortional buckling and long half-wavelength lateral-torsional buckling or a combination of these before failure. ABAQUS software based on finite element analysis is used to analyse the interaction behaviour of these buckling modes in this study. The finite element model, after calibration with experimental results available in the literature, is used to perform parametric studies, to evaluate the behaviour and strength of such beams under different types of interactions due to variation of material and member properties. The large volume of synthetic data thus generated over a range of failure modes along with the available test results are used to evaluate different equations for calculating the strength of such cold-formed lipped channel beams. Based on the comparison, a method for the design of lipped channel beams failing under the interaction of local, distortional and overall lateral torsional buckling is recommended.     

Two-Symbol FPGA Architecture for Fast Arithmetic Encoding in JPEG 2000

JPEG 2000 is one of the most popular image compression standards offering significant performance advantages over previous image standards. High computational complexity of the JPEG 2000 algorithms makes it necessary to employ methods that overcomes the bottlenecks of the system and hence an efficient solution is imperative. One such crucial algorithms in JPEG 2000 is arithmetic coding and is completely based on bit level operations. In this paper, an efficient hardware implementation of arithmetic coding is proposed which uses efficient pipelining and parallel processing for intermediate blocks. The idea is to provide a two-symbol coding engine, which is efficient in terms of performance, memory and hardware. This architecture is implemented in Verilog hardware definition language and synthesized using Altera field programmable gate array. The only memory unit used in this design is a FIFO (first in first out) of 256 bits to store the CX-D pairs at the input, which is negligible compared to the existing arithmetic coding hardware designs. The simulation and synthesis results show that the operating frequency of the proposed architecture is greater than 100 MHz and it achieves a throughput of 212 Msymbols/sec, which is double the throughput of conventional one-symbol implementation and enables at least 50% throughput increase compared to the existing two-symbol architectures.     

Highly selective SAPO 34 membrane on surface modified clay–alumina tubular support for H2/CO2 separation

The formation and growth of Silicoaluminophosphate (SAPO 34) zeolite membrane was developed on the low cost indigenous clay–alumina tubular substrate modified by cationic polymer as an intermediate layer. The amino (–NH₂) and hydroxyl (–OH) groups on the backbone of polymer are responsible for hydrogen bonding between zeolite surface and support surface. Seeding of the support surface accelerates the zeolite crystallization and enhances the formation of homogenous SAPO 34 membrane layer. The seeds were synthesized by hydrothermal process and used to provide nucleation for the membrane growth. The synthesized membranes along with seed crystals were characterized by XRD, FTIR, FESEM, TEM and EDAX analysis. The performance of the membrane formed was evaluated by single gas as well as mixture gas permeation measurement for H₂ and CO₂. The H₂/CO₂ separation selectivity of the membrane increased upto 5.88 at room temperature which is more than reported values and the reproducibility towards gas permeation and selectivity of SAPO 34 membrane shows excellent result.     

Chemical enhancement of secondary ion emission from Cd ion bombarded Mo, Ti, Al and Cu surfaces

The variation of secondary ion intensity with target current was found to be linear and parabolic for low and medium current densities, respectively. A semi-empirical formula was proposed earlier to correlate these two effects. With further increase of target current density, it is found that the secondary ion yield varies approximately as cube of the primary current density. This enhanced ion emission has been attributed to the chemical enhancement effect caused by reactive ion bombardment. The semi-empirical formula has accordingly been modified in order to take into consideration the above effect. The combinations Cd⁺-Mo, Cd⁺-Ti, Cd⁺-Al and Cd⁺-Cu have been studied and the results are explained in terms of the modified semi-empirical formula. Our observations have further been supported by ion yield versus bombarding energy studies for the combinations: Cd⁺-Mg, Cd⁺-Ti and Cd⁺-Zr.     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.