Context matters: The structure of task goals affects accuracy in multiple-target visual search

Career visual searchers such as radiologists and airport security screeners strive to conduct accurate visual searches, but despite extensive training, errors still occur. A key difference between searches in radiology and airport security is the structure of the search task: Radiologists typically scan a certain number of medical images (fixed objective), and airport security screeners typically search X-rays for a specified time period (fixed duration). Might these structural differences affect accuracy? We compared performance on a search task administered either under constraints that approximated radiology or airport security. Some displays contained more than one target because the presence of multiple targets is an established source of errors for career searchers, and accuracy for additional targets tends to be especially sensitive to contextual conditions. Results indicate that participants searching within the fixed objective framework produced more multiple-target search errors; thus, adopting a fixed duration framework could improve accuracy for career searchers.     

Computer aided detection system for micro calcifications in digital mammograms

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications ‘patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time

This paper deals with the problem of scheduling a flow shop operating in a sequence dependent setup time environment. The objective is to determine the sequence that minimises the makespan. Two efficient neighbourhood search-based heuristics have been developed and tested using 960 problems, and the results obtained reveal their usefulness. The algorithms make use of two existing constructive heuristics. A neighbourhood search known as variable neighbourhood descent is used to improve the two constructive heuristics. Experimentation is carried out on the 96 groups of problems with 10 problem instances in each group. Performance analysis is carried out using the relative performance improvement of each heuristic. The analysis shows a consistently better performance of the neighbourhood-based improvement heuristics. A paired comparison test is used for validating the superiority of the proposed heuristics. The statistical analysis reveals that the performance of the neighbourhood-based heuristics is very much dependent on the initial constructive heuristics used.     

Tabu-based GIS for solving the vehicle routing problem

Besides being a hard combinatorial problem, the VRP is also a spatial problem. Hence, effective decision making in this field strongly requires the integration of GIS and optimization systems (GIS-O). This article integrates GIS and optimization tools for solving the vehicle routing problem with loading and distance requirements (DCVRP). A general outline of the multi-step integration is pointed out showing the interaction of the GIS and the spatial optimization according to the loose coupling strategy. The computational performance of the TS-VRP algorithm for the DCVRP turned out to be quite efficient on both computation time and solution quality. The Tunisian case study well illustrates the incentive behind using such a spatial decision support system that allows the management of the problem from the data acquisition to the visualization of possible simulation scenarios in a more realistic way.     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.     

基于群体动力学的协同过滤算法及应用

针对传统协同过滤算法稀疏矩阵和推荐精度不高的问题,根据一种社会心理学模型提出了基于群体动力学的协同过滤算法.该算法综合考虑了个体因素和环境因素对用户评分行为的影响,以此来调整传统的评分预测方法,然后为用户进行推荐.实验结果表明,该算法可以明显地提高推荐的精确度,有效地缓解稀疏矩阵问题;同时该算法还可以有效减少积累误差.最后将该算法成功运用在西安景点的推荐服务上.     

基于RKNN的选址算法及其应用研究

为了使各个设施的整体利用效果最佳, 结合空间位置影响力查询技术, 提出了基于RKNN的选址算法。针对空间对象对空间位置的贡献具有多重性, 建立了一种新的空间位置影响力的度量模型, 该模型更加符合实际应用情况; 然后, 利用该模型计算选址方案中各个设施的影响力, 并引入均衡系数评价选址方案的合理性, 均衡系数越小, 方案越合理。实际应用表明基于RKNN的选址算法使村镇基础设施的选址更加合理、有效。     

求解工程结构优化问题的改进布谷鸟搜索算法

针对布谷鸟搜索算法局部搜索能力不强的缺点, 提出一种基于随机局部搜索的改进布谷鸟搜索算法用于求解工程结构优化问题。引入惯性权重以平衡算法的勘探和开采能力; 利用随机局部搜索方法对当前最优解进行局部搜索, 以加快算法的收敛速度。两个工程结构优化问题的实验结果表明了该算法的可行性和有效性。     

面向旋翼无人机的高压输电线在线检测方法

面向旋翼无人机电力巡线的高压输电线在线检测对算法实时性、鲁棒性要求高的问题,提出了一种基于边界搜索和Radon变换(BSRT)的高压输电线识别算法.分析了高压输电线在图像中的边界贯穿特征,提出了以图像四条边界作为起始点的搜索策略,给出了边界约束下的直线特征Radon变换能量函数和求解方法.复杂度分析结果表明本算法与经典的Radon算法相比,复杂度降低了一个数量级.以人工合成图像和无人机实际航拍图像,对本算法、Radon算法和LSD算法在实时性和有效性方面进行了实验对比分析,实验结果表明,本算法的处理速度较Radon算法有很大的提高,与LSD算法的处理速度基本处于同一量级,但本算法的高压输电线检测精度大幅优于传统的Radon和LSD算法.理论分析及实验结果证明,提出的BSRT方法有效地解决了经典Radon算法的高复杂度和LSD算法的复杂背景高敏感性的问题,具有较好的应用价值.