Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction Using Filter-Based Feature Selection Methods

Classification techniques is a popular approach to predict software defects and it involves categorizing modules, which is represented by a set of metrics or code attributes into fault prone (FP) and non-fault prone (NFP) by means of a classification model. Nevertheless, there is existence of low quality, unreliable, redundant and noisy data which negatively affect the process of observing knowledge and useful pattern. Therefore, researchers need to retrieve relevant data from huge records using feature selection methods. Feature selection is the process of identifying the most relevant attributes and removing the redundant and irrelevant attributes. In this study, the researchers investigated the effect of filter feature selection on classification techniques in software defects prediction. Ten publicly available datasets of NASA and Metric Data Program software repository were used. The topmost discriminatory attributes of the dataset were evaluated using Principal Component Analysis (PCA), CFS and FilterSubsetEval. The datasets were classified by the selected classifiers which were carefully selected based on heterogeneity. Naïve Bayes was selected from Bayes category Classifier, KNN was selected from Instance Based Learner category, J48 Decision Tree from Trees Function classifier and Multilayer perceptron was selected from the neural network classifiers. The experimental results revealed that the application of feature selection to datasets before classification in software defects prediction is better and should be encouraged and Multilayer perceptron with FilterSubsetEval had the best accuracy. It can be concluded that feature selection methods are capable of improving the performance of learning algorithms in software defects prediction

Effect of intranasally administered insulin on cerebral blood flow and perfusion:a randomized experiment in young and older adults

Pathophysiology, epidemiology and therapy of agein

Effects of intranasal insulin application on the hypothalamic BOLD response to glucose ingestion

Abstract The hypothalamus is a crucial structure in the brain that responds to metabolic cues and regulates energy homeostasis. Patients with type 2 diabetes demonstrate a lack of hypothalamic neuronal response after glucose ingestion, which is suggested to be an underlying cause of the disease. In this study, we assessed whether intranasal insulin can be used to enhance neuronal hypothalamic responses to glucose ingestion. In a randomized, double-blinded, placebo-controlled 4-double cross-over experiment, hypothalamic activation was measured in young non- diabetic subjects by determining blood-oxygen-level dependent MRI signals over 30 minutes before and after ingestion of 75 g glucose dissolved in 300 ml water, under intranasal insulin or placebo condition. Glucose ingestion under placebo condition lead to an average 1.4% hypothalamic BOLD decrease, under insulin condition the average response to glucose was a 2.2% decrease. Administration of water did not affect the hypothalamic BOLD responses. Intranasal insulin did not change circulating glucose and insulin levels. Still, circulating glucose levels showed a significant dampening effect on the BOLD response and insulin levels a significant strengthening effect. Our data provide proof of concept for future experiments testing the potential of intranasal application of insulin to ameliorate defective homeostatic control in patients with type 2 diabetes

Interrelationships Between Pituitary Hormones as Assessed From 24-hour Serum Concentrations in Healthy Older Subjects

Context: Hormones of the hypothalamic-pituitary-target gland axes are mostly investigated separately, whereas the interplay between hormones might be as important as each separate hormonal axis.Objective: Our aim is to determine the interrelationships between GH, TSH, ACTH, and cortisol in healthy older individuals.Design: We made use of 24-hour hormone serum concentrations assessed with intervals of 10 minutes from 38 healthy older individuals with a mean age (SD) of 65.1 (5.1) years from the Leiden Longevity Study. Cross-correlation analyses were performed to assess the relative strength between 2 24-hour hormone serum concentration series for all possible time shifts. Cross-approximate entropy was used to assess pattern synchronicity between 2 24-hour hormone serum concentration series.Results: Within an interlinked hormonal axis, ACTH and cortisol were positively correlated with a mean (95% confidence interval) correlation coefficient of 0.78 (0.74-0.81) with cortisol following ACTH concentrations with a delay of 10 minutes. Between different hormonal axes, we observed a negative correlation coefficient between cortisol and TSH of -0.30 (-0.36 to -0.25) with TSH following cortisol concentrations with a delay of 170 minutes. Furthermore, a positive mean (95% confidence interval) correlation coefficient of 0.29 (0.22-0.37) was found between TSH and GH concentrations without any delay. Moreover, cross-approximate entropy analyses showed that GH and cortisol exhibit synchronous serum concentration patterns.Conclusions: This study demonstrates that interrelations between hormones from interlinked as well as different hypothalamic-pituitary-target gland axes are observed in healthy older individuals. More research is needed to determine the biological meaning and clinical consequences of these observations.Pathophysiology, epidemiology and therapy of agein

Familial Longevity Is Marked by Lower Diurnal Salivary Cortisol Levels: The Leiden Longevity Study

BACKGROUND: Reported findings are inconsistent whether hypothalamic-pituitary-adrenal (HPA) signaling becomes hyperactive with increasing age, resulting in increasing levels of cortisol. Our previous research strongly suggests that offspring from long-lived families are biologically younger. In this study we assessed whether these offspring have a lower HPA axis activity, as measured by lower levels of cortisol and higher cortisol feedback sensitivity. METHODS: Salivary cortisol levels were measured at four time points within the first hour upon awakening and at two time points in the evening in a cohort comprising 149 offspring and 154 partners from the Leiden Longevity Study. A dexamethasone suppression test was performed as a measure of cortisol feedback sensitivity. Age, gender and body mass index, smoking and disease history (type 2 diabetes and hypertension) were considered as possible confounding factors. RESULTS: Salivary cortisol secretion was lower in offspring compared to partners in the morning (Area Under the Curve = 15.6 versus 17.1 nmol/L, respectively; p = 0.048) and in the evening (Area Under the Curve = 3.32 versus 3.82 nmol/L, respectively; p = 0.024). Salivary cortisol levels were not different after dexamethasone (0.5 mg) suppression between offspring and partners (4.82 versus 5.26 nmol/L, respectively; p = 0.28). CONCLUSION: Offspring of nonagenarian siblings are marked by a lower HPA axis activity (reflected by lower diurnal salivary cortisol levels), but not by a difference in cortisol feedback sensitivity. Further in-depth studies aimed at characterizing the HPA axis in offspring and partners are needed

Comparative Analysis of the Equivital EQ02 Lifemonitor with Holter Ambulatory ECG Device for Continuous Measurement of ECG, Heart Rate, and Heart Rate Variability: A Validation Study for Precision and Accuracy

Background: The Equivital (EQ02) is a multi-parameter telemetric device offering both real-time and/or retrospective, synchronized monitoring of ECG, HR and HRV, respiration, activity and temperature. Unlike the Holter, which is the gold standard for continuous ECG measurement, EQO2 continuously monitors ECG via electrodes interwoven in the textile of a wearable belt.Objective: To compare EQ02 with the Holter for continuous home measurement of ECG, heart rate (HR) and heart rate variability (HRV).Methods: Eighteen healthy participants wore, simultaneously for 24 hours, the Holter and EQ02 monitors. Per participant, averaged HR and HRV per 5 minutes from the two devices were compared using Pearson correlation, paired T-test and Blant-Altman analyses. Accuracy and precision metrics included mean absolute relative difference (MARD). Results: Artefact content of EQ02 data varied widely between (range 1.93% to 56.45%) and within (range 0.75% to 99.61%) participants. Comparing the EQ02 to the Holter, the Pearson correlations were respectively 0.724, 0.955 and 0.997 for datasets containing all data and data with <50% or <20% artefacts respectively. For datasets containing respectively all data, data with <50% or <20% artefacts, bias estimated by Bland-Altman analysis was -2.8, -1.0 and -0.8 beats per minute and 24h MARD was 7.08, 3.01 and 1.5. After selecting a three- hour stretch of data containing 1.15% artefacts, Pearson correlation was 0.786 for HRV measured as standard deviation of NN intervals (SDNN). Conclusions:Although the EQ02 can accurately measure ECG and HRV, its accuracy and precision is highly dependent on artefact content. This is a serious limitation for clinical use in individual patients. However, the advantages of the EQ02 (ability to simultaneously monitor several physiologic parameters) may outweigh its disadvantages (higher artefact load) for research purposes and/ or for home monitoring in larger groups of study participants. Further studies can be aimed at minimizing the artefacts

Associations between insulin action and integrity of brain microstructure differ with familial longevity and with age

Impaired glucose metabolism and type 2 diabetes have been associated with cognitive decline, dementia, and with structural and functional brain features. However, it is unclear whether these associations differ in individuals that differ in familial longevity or age. Here, we investigated the association between parameters of glucose metabolism and microstructural brain integrity in offspring of long-lived families (offspring) and controls; and age categories thereof. From the Leiden Longevity Study, 132 participants underwent oral glucose tolerance test to assess glycemia (fasted glucose and glucose area-under-the-curve (AUC)), insulin resistance (fasted insulin, AUCinsulin, and homeostatic model assessment of insulin resistance (HOMA-IR)), and pancreatic Beta cell secretory capacity (insulinogenic index). 3Tesla MRI and Magnetization Transfer (MT) imaging MT-ratio peak-height was used to quantify differences in microstructural brain parenchymal tissue homogeneity that remain invisible on conventional MRI. Analyses were performed in offspring and age-matched controls, with and without stratification for age.In the full offspring group only, reduced peak-height in grey and white matter was inversely associated with AUCinsulin, fasted insulin, HOMA-IR and insulinogenic-index (all p<0.01). When dichotomised for age (≤65years & >65 years): in younger controls, significantly stronger inverse associations were observed between peak-height and fasted glucose, AUCglucose, fasted insulin, AUCinsulin and HOMA-IR in grey matter; and for AUCglucose, fasted insulin and HOMA-IR in white matter (all P-interaction<0.05). Although the strength of the associations tended to attenuate with age in the offspring group, the difference between age groups was not statistically significant. Thus, associations between impaired insulin action and reduced microstructural brain parenchymal tissue homogeneity were stronger in offspring compared to controls, and seemed to diminish with age

Subclinical hypothyroidism and cognitive function in people over 60 years:a systematic review and meta-analysis

Subclinical hypothyroidism (SCH), defined as elevated thyroid stimulating hormone and normal thyroid hormone levels, and cognitive impairment are both common in older people. While the relation between overt hypothyroidism and cognitive impairment is well established, data on the association between SCH and cognitive impairment are conflicting. This systematic review and meta- analysis was performed to assess available evidence on the association of SCH with cognition in community dwelling, relatively healthy older adults.PubMed, EMBASE, Web of Science, COCHRANE, CINAHL, PsycINFO and Academic Search Premier (January 1966 to April 1, 2015) were searched without language restrictions, as were references of key articles, for studies on the association between SCH and cognition in older adults (>60 years). These studies were reviewed by two independent reviewers according to predefined criteria for eligibility and methodological quality, and data extracted using standardized forms.Of the 844 reports initially identified, 270 remained after exclusion of duplicates. Of the 270, fifteen studies comprising 19, 944 subjects, of whom 1, 199 had subclinical hypothyroidism were included. Data from the 15 studies was pooled, and meta- analyzed cross-sectionally for global cognition (MMSE), executive function, and memory, using random effects models. Pooled effect size (ES) for MMSE was -0.01 (95% CI -0.09, 0.08), with heterogeneity (I2) of 55.1%. Pooled ES was <0.001 (95% CI -0.10, 0.09) for executive function (I2 = 13.5%), and 0.01 (95% CI -0.12, 0.14) for memory (I2 = 46.9%). In addition, prospective analysis including four studies showed pooled ES of 0.033 (95% CI -0.001-0.067) for MMSE (I2 <0.001%), indicating that subclinical hypothyroidism was not significantly associated with accelerated cognitive decline.This systematic review and meta-analysis provides no evidence that supports an association between SCH and cognitive impairment in relatively healthy older adults

Empirical analysis of tree-based classification models for customer churn prediction

Customer churn is a vital and reoccurring problem facing most business industries, particularly the telecommunications industry. Considering the fierce competition among telecommunications firms and the high expenses of attracting and gaining new subscribers, keeping existing loyal subscribers becomes crucial. Early prediction of disgruntled subscribers can assist telecommunications firms in identifying the reasons for churn and in deploying applicable innovative policies to boost productivity, maintain market competitiveness, and reduce monetary damages. Controlling customer churn through the development of efficient and dependable customer churn prediction (CCP) solutions is imperative to attaining this goal. According to the outcomes of current CCP research, several strategies, including rule-based and machine-learning (ML) processes, have been proposed to handle the CCP phenomenon. However, the lack of flexibility and robustness of rule based CCP solutions is a fundamental shortcoming, and the lopsided distribution of churn datasets is deleterious to the efficacy of most traditional ML techniques in CCP. Regardless, ML-based CCP solutions have been reported to be more effective than other forms of CCP solutions. Unlike linear-based, instance-based, and function-based ML classifiers, tree-based ML classifiers are known to generate predictive models with high accuracy, high stability, and ease of interpretation. However, the deployment of tree-based classifiers for CCP is limited in most cases to the decision tree (DT) and random forest (RF). Hence, this research investigated the effectiveness of tree-based classifiers with diverse computational properties in CCP. Specifically, the CCP performances of diverse tree-based classifiers such as the single, ensemble, enhanced, and hybrid tree-based classifiers are investigated. Also, the effects of data quality problems such as the class imbalance problem (CIP) on the predictive performances of tree-based classifiers and their homogeneous ensemble variants on CCP were assessed. From the experimental results, it was observed that the investigated tree-based classifiers outperformed other forms of classifiers such as linear-based (Support Vector Machine (SVM)), instance-based (K-Nearest Neighbour (KNN)), Bayesian-based (Naïve Bayes (NB)) and function-based (MultiLayer Perceptron (MLP)) classifiers in most cases with or without the CIP. Also, it was observed that the CIP has a significant effect on the CCP performances of investigated tree-based classifiers, but the combination of a data sampling technique and a homogeneous ensemble method can be an effective solution to CIP and also generate efficient CCP models