255 research outputs found
Internet and distributed computing advancements : theoretical frameworks and practical applications
No full textAs software and computer hardware grows in complexity, networks have grown to match. The increasing scale, complexity, heterogeneity, and dynamism of communication networks, resources, and applications has made distributed computing systems brittle, unmanageable, and insecure.
Internet and Distributed Computing Advancements: Theoretical Frameworks and Practical Applications is a vital compendium of chapters on the latest research within the field of distributed computing, capturing trends in the design and development of Internet and distributed computing systems that leverage autonomic principles and techniques. The chapters provided within this collection offer a holistic approach for the development of systems that can adapt themselves to meet requirements of performance, fault tolerance, reliability, security, and Quality of Service (QoS) without manual intervention
Enhanced divide-and-conquer algorithm with 2-block policy
No full textThe number of comparisons involved in searching minimum
and maximum elements from a set of data will determine
the performance of an algorithm. A Divide-and-Conquer
algorithm is the most efficient algorithm for searching
minimum and maximum elements of a set of data of any
size. However, the performance of this algorithm can still
be improved by reducing the number of comparisons of
certain sets of data. In this paper a 2-block (2B) policy
under the divide-and-conquer technique is proposed in
order to deal with this problem. On the basis of this policy,
the divide-and-conquer algorithm is enhanced. It is shown
that the performance of the proposed algorithm performs
equally at par when compared with the established
algorithm of data size of power of two and better when
compared with data size of not a power of two
A method of estimating aborted transaction in the database concurrency control system
No full textTransactions may be aborted when they are unable to
obtain a lock on a required data item. Estimating the
proportion of transaction that aborts is one of the key
issues in modelling a system which affect the performance
measures of interest such as average response time and the
throughput capacity of the system. This paper shows a
method of estimating aborted transaction and performs a
comparative study with other method given by Mitrani et
al
An efficient perceptual color indexing method for content-based image retrieval using uniform color space
No full textDominant Color Descriptor (DCD) is one of the famous descriptors in Content-based image retrieval (CBIR).Sequential search is one of the common drawbacks of most color descriptors especially in large databases.In this paper, dominant colors of an image are indexed to avoid sequential search in the database where uniform RGB color space is used to index images in LUV perceptual color space.Proposed indexing method will speed up the retrieval process where the dominant colors in query image are used to reduce the search space.Additionally, the accuracy of color descriptors is improved due to this space reduction. Experimental results show effectiveness of the proposed color indexing method in reducing search space to less than 25 % without degradation the accuracy
Distinguishing twins by Gait via Jackknife-Like validation in classification analysis
No full textThis paper is about analysing the uniqueness of twins by gait biometric. The motivation arises due to twins, having facial similarity may lay difficulties to a video-based recognition system employing face biometric. Gait, a biometric based on the way a person walk, can perhaps be a useful descriptor. Due to the small size data set, classification via leave-one-out cross validation may not be sufficient to test gait’s viability as a descriptor for twins. Thus, this paper proposes a jackknife-like validation in a matched-pair classification. Comparing between the results of both validation approaches, results of the proposed method have shown to be promising. The results perhaps may point to the uniqueness of each individual twin by gait biometric
Parallel full HD video decoding for multicore architecture
No full textNowadays, the multicore architecture is adopted everywhere in the design of contemporary processors in order to boost up the performance of multitasking applications. This paper mainly exploits the multicore capability for full HD video decoding speedup to meet realtime display. Hantro 6100 H.264 decoder is chosen as the reference decoder. The serial decoding algorithm in the Hantro 6100 H.264 decoder is replaced with a parallel decoding algorithm. In this research work, macroblock level parallelism is implemented using the enhanced version of macroblock region partitioning (MBRP) is implemented for the parallel video decoding of H.264 video. The results show that the workloads are well-balanced among the processor cores. It is observed that the maximum speedup values are attained when the decoder is running with 4 threads on a 4 core system and 8 logical core system configuration. Moreover, it is also observed that there is no degradation of visual quality throughout the decoding process
Optimization of ANFIS Using Artificial Bee Colony Algorithm for Classification of Malaysian SMEs
No full textAdaptive Neuro-Fuzzy Inference System (ANFIS) has been widely applied in industry as well as scientific problems. This is due to its ability to approximate every plant with proper number of rules. However, surge in auto-generated rules, as the inputs increase, adds up to complexity and computational cost of the network. Therefore, optimization is required by pruning the weak rules while, at the same time, achieving maximum accuracy. Moreover, it is important to note that over-reducing rules may result in loss of accuracy. Artificial Bee Colony (ABC) is widely applied swarm-based technique for searching optimum solutions as it uses few setting parameters. This research explores the applicability of ABC algorithm to ANFIS optimization. For the practical implementation, classification of Malaysian SMEs is performed. For validation, the performance of ABC is compared with one of the popular optimization techniques Particle Swarm Optimization (PSO) and recently developed Mine Blast Algorithm (MBA). The evaluation metrics include number of rules in the optimized rule-base, accuracy, and number of iterations to converge. Results indicate that ABC needs improvement in exploration strategy in order to avoid trap in local minima. However, the application of any efficient metaheuristic with the modified two-pass ANFIS learning algorithm will provide researchers with an approach to effectively optimize ANFIS when the number of inputs increase significantly
ELP-M2: An Efficient Model for Mining Least Patterns from Data Repository
No full textMost of the algorithm and data structure facing a computational problem when they are required to deal with a highly sparse and dense dataset. Therefore, in this paper we proposed a complete model for mining least patterns known as Efficient Least Pattern Mining Model (ELP-M2) with LP-Tree data structure and LP-Growth algorithm. The comparative study is made with the well-know LP-Tree data structure and LP-Growth algorithm. Two benchmarked datasets from FIMI repository called Kosarak and T40I10D100K were employed. The experimental results with the first and second datasets show that the LP-Growth algorithm is more efficient and outperformed the FP-Growth algorithm at 14% and 57%, respectively
Training ANFIS Using Catfish-Particle Swarm Optimization for Classification
No full textANFIS performance depends on the parameters it is trained with. Therefore, the training mechanism needs to be faster and reliable. Many have trained ANFIS parameters using GD, LSE, and metaheuristic techniques but the efficient one are still to be developed. Catfish-PSO algorithm is one of the latest successful swarm intelligence based technique which is used in this research for training ANFIS. As opposed to standard PSO, Catfish-PSO has string exploitation and exploration capability. The experimental results of training ANFIS network for classification problems show that Catfish-PSO algorithm achieved much better accuracy and satisfactory results
Prediction of Malaysian–Indonesian Oil Production and Consumption Using Fuzzy Time Series Model
No full textFuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.</jats:p
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