Swarm optimized organizing map (SWOM): A swarm intelligence based optimization of self-organizing map

WOS: 000266851000051This work studies the optimization of SOM algorithm in terms of reducing its training time by the use of a swarm intelligence method, i.e. particle swarm optimization (PSO). Our novel algorithm optimizes SOM with PSO and reduces computational time of the training phase of SOM significantly. The performance of the algorithms has been tested with genomic datasets. biomedical datasets and an artificial dataset to show the efficiency of swarm optimized SOM, i.e. SWOM. The experimental comparison between SOM and SWOM, has demonstrated significant reduction in training time of SWOM with preservation of clustering quality. (C) 2009 Elsevier Ltd. All rights reserved

Majority vote feature selection algorithm in software fault prediction

Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance. © 2019, ComSIS Consortium. All rights reserved

Cardiotocogram Data Classification using Random Forest based Machine Learning Algorithm

The Cardiotocography is the most broadly utilized technique in obstetrics practice to monitor fetal health condition. The foremost motive of monitoring is to detect the fetal hypoxia at early stage. This modality is also widely used to record fetal heart rate and uterine activity. The exact analysis of cardiotocograms is critical for further treatment. In this manner, fetal state evaluation utilizing machine learning technique using cardiotocogram data has achieved significant attention. In this paper, we implement a model based CTG data classification system utilizing a supervised Random Forest (RF) which can classify the CTG data based on its training data. As per the showed up results, the overall performance of the supervised machine learning based classification approach provided significant performance. In this study, Precision, Recall, F-Score and Rand Index has been employed as the metric to evaluate the performance. It was found that, the RF based classifier could identify normal, suspicious and pathologic condition, from the nature of CTG data with 94.8% accuracy

urinary tract symptom severity in elderly men

The aim of this study was to evaluate the relationship between lower urinary tract symptoms (LUTSs), erectile dysfunction (ED) and symptomatic late-onset hypogonadism (SLOH) in ageing men in the Aegean region of Turkey. Five hundred consecutive patients >40 years old who had been in a steady sexual relationship for the past 6 months and were admitted to one of six urology clinics were included in the study. Serum prostate-specific antigen and testosterone levels and urinary flow rates were measured. All patients filled out the International Prostate Symptom Score and Quality of Life (IPSS-QoL), International Index of Erectile Function (IIEF) and Aging Males' Symptoms (AMS) scale forms. Of the patients, 23.9% had mild LUTSs, 53.3% had moderate LUTSs and 22.8% had severe LUTSs. The total testosterone level did not differ between groups. Additionally, 69.6% had ED. The presence of impotence increased with increasing LUTS severity. Symptomatic late-onset hypogonadism (AMS>27) was observed in 71.2% of the patients. The prevalence of severe hypogonadism symptoms increased with the IPSS scores. A correlation analysis revealed that all three questionnaire scores were significantly correlated. In conclusion, LUTS severity is an age-independent risk factor for ED and SLOH. LUTS severity and SLOH symptoms appear to have a strong link that requires etiological and biological clarification in future studies