Modified Normalized Least Mean Square (MNLMS) algorithm, which is a sign form of NLMS based on set-membership (SM) theory in the class of optimal bounding ellipsoid (OBE) algorithms, requires a priori knowledge of error bounds that is unknown in most applications. In a special but popular case of measurement noise, a simple algorithm has been proposed. With some simulation examples the performance of algorithm is compared with MNLMS.

Imperialist Competitive algorithm (ICA) is considered as prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA، at run time، the suggested method (ACICA) finds the optimum number of clusters while optimal clustering of the data simultaneously. To increase the accuracy and speed of convergence، the structure of ICA changes. The proposed algorithm requires no background knowledge to classify the data. In addition، the proposed method is more accurate in comparison with other clustering methods based on evolutionary algorithms. DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method، the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering algorithms based on evolutionary algorithms.

and standard method for evaluating scoliosis is Cobb angle measurement, but several studies have shown that there is intra-and inter-observer variation in measuring cobb angle manually. Objective: Develop a computer-assisted system to decrease operator-dependent errors in Cobb angle measurement. Methods: The spinal cord in the given x-ray image of the spine is highlighted using contract-stretching technique. The overall structural curvature of the spine is determined by a semi-automatic algorithm aided by the operator. Once the morphologic curve of the spine is determined, in the last step the cobb-angle is estimated by calculating the angle between two normal lines to the spinal curve at the inflection points of the curve. Results: Evaluation results of the developed algorithms using 14 radiographs of patients (4-40 years old) with cobb angle ranges from 34-82 degrees, revealed that the developed algorithm accurately estimated cobb angle. Statistical analysis showed that average angle values estimated using the developed method and that provided by experts are statistically equal. The correlation coefficient between the angle values estimated using the developed algorithm and those provided by the expert is 0. 81. Conclusion: Compared with previous algorithms, the developed system is easy to use, less operator-dependent, accurate, and reliable. The obtained results are promising and show that the developed computer-based system could be used to quantify scoliosis by measuring Cobb angle.

In the real world clustering problems, it is often encountered to perform cluster analysis on data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. In addition, traditional methods, for example, the K-means algorithm, usually ask the user to provide the number of clusters. In this paper, we propose a new method to cluster mixed data and automatically evolve the number of clusters as well as clustering of data set. In the proposed method, Davies-Bouldin Index is used as fitness function and we use the genetic algorithm to optimize fitness function. Also, we use a more accurate distance measure for calculating the distance between categorical values. The performance of this algorithm has been studied on real world and simulated data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach.

The domain of Very Large Scale Integrated (VLSI) circuit testing has experienced advancements in methodologies for fault detection, which are crucial for addressing the complexities of modern applications. Traditional testing techniques frequently fall short in achieving precise fault localization, highlighting the necessity for the development of enhanced methodologies. This paper endeavors to investigate the implementation of an optimized test generation algorithm aimed at improving testing efficiency and increasing fault coverage. In this study, we utilized a (PSO)-enhanced FAN (fan-out-oriented algorithm) applied to ISCAS'89 benchmark circuits. The optimization process involved refining the decision trees utilized in the FAN algorithm through a rigorously defined cost function, which strikes a balance between accuracy, complexity, and operational efficiency. The results indicate improvement in both fault coverage and detection efficiency. Furthermore, the optimization process resulted in a reduction of required test vectors, thereby enhancing testing efficiency, particularly in high-volume production environments. The study also introduces the development of diagnostic metrics that provide deeper insights into circuit failures and proposes design-for-testability techniques to improve reliability. The integration of PSO within the FAN algorithm not only facilitates the generation of robust test solutions but also produces high-quality test vectors.

In this paper a novel technique for automatic data clustering based on the artificial immune algorithm is proposed. The lengths of the antibodies are dynamically changed based on inter-clusters and intra-clusters distances by means of a fuzzy controller which has been added to the immune algorithm to provide, also, a soft computing approach for data clustering. This idea leads to proper number of clusters and effective and powerful clustering process without any additional try and error efforts. Also the manual setting of the number of clusters is available in the proposed algorithm (like other unsupervised clustering approaches) after removing the fuzzy controller from the proposed clustering system. The method has been tested on the different kinds of the complex artificial data sets and well known benchmarks. The experimental results show that the performance of the proposed technique is much better than the k-means clustering algorithm (as a conventional one), specially for huge data sets with large feature vector dimensions. Furthermore, it is found that the performance of the proposed approach is comparable, sometimes better than the genetic algorithm based clustering technique (as an evolutionary clustering algorithm).

Spatial data collection is a time/money consuming task in activities related to geospatial sciences. Spatial data integration increases the efficiency of spatial data through cost reduction. Therefore, developing matching algorithms is of vital importance in spatial data integration, data updating and data accuracy enhancement. The main objective of this research is to design and develop a semi-automatic vector matching system. The developed system simulates human brain process in its matching procedure. Priliminary results are promissing. Further reaseaches are required to examine the system efficiency.