Multi-swarm bat algorithm for association rule mining using multiple cooperative strategies

Association Rule Mining (ARM) can be considered as a combinatorial problem with the purpose of extracting the correlations between items in sizeable datasets. The numerous polynomial exact algorithms already proposed for ARM are unadapted for large databases and especially for those existing on the web. Assuming that datasets are a large space search, intelligent algorithms was used to found high quality rules and solve ARM issue. This paper deals with a cooperative multi-swarm bat algorithm for association rule mining. It is based on the bat-inspired algorithm adapted to rule discovering problem (BAT-ARM). This latter suffers from absence of communication between bats in the population which lessen the exploration of search space. However, it has a powerful rule generation process which leads to perfect local search. Therefore, to maintain a good trade-off between diversification and intensification, in our proposed approach, we introduce cooperative strategies between the swarms that already proved their efficiency in multi-swarm optimization algorithm(Ring, Master-slave). Furthermore, we innovate a new topology called Hybrid that merges Ring strategy with Master-slave plan previously developed in our earlier work [23]. A series of experiments are carried out on nine well known datasets in ARM field and the performance of proposed approach are evaluated and compared with those of other recently published methods. The results show a clear superiority of our proposal against its similar approaches in terms of time and rule quality. The analysis also shows a competitive outcomes in terms of quality in-face-of multi-objective optimization methods.     

Performance Improvement of Various Types of Induction-based Wind Farms Using Center-node Unified Power Flow Controller

In this paper, we propose the use of center-node unified power flow controller (C-UPFC) for improving the performance of different types of wind farms and mitigating their negative impacts on the grid. C-UPFC is considered one of the modernist members on Flexible AC Transmission System (FACTS). C-stocktickerUPFC has the ability to control several system parameters; the active and reactive power at both ends of the interconnected transmission line and the voltage at the midpoint. Three different induction-based wind farms are considered; 1) Squirrel Cage Induction Generator (SCIG), 2) Doubly Fed Induction Generator (DFIG), and 3) a combination of SCIG and DFIG turbines, i.e., Combined Wind Farm (CWF). C-stocktickerUPFC is comprehensively modelled for the first time in MATLAB Simulink, then the performance of the three wind farms is assessed with and without this device during three phase faults. Probabilistic voltage stability index (Probabilistic VSI) is used to measure the stability of the studied systems. In addition, the performance of three wind farms integrated with C-UPFC is compared with their performance when they integrated with Static synchronous compensators (STATCOM). The results show that C-UPFC has the ability to enhance the performance of wind farms during the three phase fault. C-UPFC is capable to remain the connection between SCIG wind farm and the interconnected grid during the fault. The voltage of CWF is greatly enhanced in the case of using C-UPFC. C-UPFC also improves the output powers of DFIG and CWF, especially after fault clearance.     

Packet aggregation based network I/O virtualization for cloud computing

Virtualization is a key technology to enable cloud computing. Driver domain based model for network virtualization offers isolation and high levels of flexibility. However, it suffers from poor performance and lacks scalability. In this paper, we evaluate networking performance of virtual machines within Xen. The I/O channel transferring packets between the driver domain and the virtual machines is shown to be the bottleneck. To overcome this limitation, we proposed a packet aggregation based mechanism to transfer packets from the driver domain to the virtual machines. Packet aggregation, combined with an efficient core allocation, allows virtual machines throughput to scale up by 700%, while minimizing both memory and CPU consumption. Besides, aggregation impact on packets delay and jitter remains acceptable. Hence, the proposed I/O virtualization model satisfies infrastructure providers to offer Cloud computing services.     

Membrane permeability of some model compounds through macroporous membranes

Membranes consisting of various amounts of poly(2-vinylpyridine) (PVN) and its oxide PVNO were prepared by casting from ethanol and pyridine solutions. Resultant membranes exhibited effective diffusion coefficients in the range of 10^?9?10^?11 cm² s^?1. Effective diffusion coefficients were affected by the casting solvent, and by the hydrophilic polymer (PVNO) content.     

Efficient algorithm for automatic road sign recognition and its hardware implementation

The automatic detection of road signs is an application that alerts the vehicle’s driver of the presence of signals and invites him to react on time in the aim to avoid potential traffic accidents. This application can thus improve the road safety of persons and vehicles traveling in the road. Several techniques and algorithms allowing automatic detection of road signs are developed and implemented in software and do not allow embedded application. We propose in this work an efficient algorithm and its hardware implementation in an embedded system running in real time. In this paper we propose to implement the application of automatic recognition of road signs in real time by optimizing the techniques used in different phases of the recognition process. The system is implemented in a Virtex4 FPGA family which is connected to a camera mounted in the moving vehicle. The system can be integrated into the dashboard of the vehicle. The performance of the system shows a good compromise between speed and efficiency.     

Pairwise optimized Rocchio algorithm for text categorization

This paper examines the Rocchio algorithm and its application in text categorization. Existing approaches using global parameters optimization of Rocchio algorithm result in choosing one fixed prototype representing each category for multi-category text categorization problems. Therefore, they have limited discriminating power on different category’s distribution and their parameter optimization methods are based on weak representation ability of the negative samples consisting of several categories. We present a pairwise optimized Rocchio algorithm, which dynamically adjusts the prototype position between pairs of categories. Experiments were conducted on three benchmark corpora, the 20-Newsgroup, Reuters-21578 and TDT2. The results confirm that our proposed pairwise method achieves encouraging performance improvement over the conventional Rocchio method. A comparative study with the top notch text classifier Support Vector Machine (SVM) also shows the pairwise Rocchio method achieves competitive results.     

Relevant Timed Schedules/Clock Vectors for Constructing Time Petri Net Reachability Graphs

We consider here the time Petri nets (the TPN model) and its state space abstractions. We show that only some timed schedules/clock vectors (one per enabled transition) of the clock/firing domains are relevant to construct reachability graphs for the TPN. Moreover, we prove formally that the resulting graphs are smaller than the TPN reachability graphs proposed in the literature. Furthermore, these results establish a relation between dense time and discrete time analysis of time Petri nets and allow also improving discrete time analysis by considering only some elements of the clock/firing domains.     

Discrete cooperative particle swarm optimization for FPGA placement

Particle swarm optimization (PSO) is a stochastic optimization technique that has been inspired by the movement of birds. On the other hand, the placement problem in field programmable gate arrays (FPGAs) is crucial to achieve the best performance. Simulated annealing algorithms have been widely used to solve the FPGA placement problem. In this paper, a discrete PSO (DPSO) version is applied to the FPGA placement problem to find the optimum logic blocks and IO pins locations in order to minimize the total wire-length. Moreover, a co-operative version of the DPSO (DCPSO) is also proposed for the FPGA placement problem. The problem is entirely solved in the discrete search space and the proposed implementation is applied to several well-known FPGA benchmarks with different dimensionalities. The results are compared to those obtained by the academic versatile place and route (VPR) placement tool, which is based on simulated annealing. Results show that both the DPSO and DCPSO outperform the VPR tool for small and medium-sized problems, with DCPSO having a slight edge over the DPSO technique. For higher-dimensionality problems, the algorithms proposed provide very close results to those achieved by VPR.     

MG-join: detecting phenomena and their correlation in high dimensional data streams

A phenomenon appears in a sensor network when a group of sensors continuously produces similar readings (i.e., data streams) over a period of time. This involves the processing of hundreds and maybe thousands of data streams in real-time. This paper focuses on detecting environmental phenomena and determining possible correlation between such phenomena.     

Understanding hand gestures using approximate graph matching

We live in a society that depends on high-tech devices for assistance with everyday tasks, including everything from transportation to health care, communication, and entertainment. Tedious tactile input interfaces to these devices result in inefficient use of our time. Appropriate use of natural hand gestures will result in more efficient communication if the underlying meaning is understood. Overcoming natural hand gesture understanding challenges is vital to meet the needs of these increasingly pervasive devices in our every day lives. This work presents a graph-based approach to understand the meaning of hand gestures by associating dynamic hand gestures with known concepts and relevant knowledge. Conceptual-level processing is emphasized to robustly handle noise and ambiguity introduced during generation, data acquisition, and low-level recognition. A simple recognition stage is used to help relax scalability limitations of conventional stochastic language models. Experimental results show that this graph-based approach to hand gesture understanding is able to successfully understand the meaning of ambiguous sets of phrases consisting of three to five hand gestures. The presented approximate graph-matching technique to understand human hand gestures supports practical and efficient communication of complex intent to the increasingly pervasive high-tech devices in our society.