Analysis of Data mining based Software Defect Prediction Techniques

Software bug repository is the main resource for fault prone modules. Different data mining algorithms are used to extract fault prone modules from these repositories. Software development team tries to increase the software quality by decreasing the number of defects as much as possible. In this paper different data mining techniques are discussed for identifying fault prone modules as well as compare the data mining algorithms to find out the best algorithm for defect prediction

Impact of structured meetings on the learning of faculty members

Objective: To determine impact of structured meetings on learning and faculty developmentMethodology: The observational cross sectional study was conducted at Bahria University Medical &Dental College from October 2010 to March 2011. Feed back of all faculty members of university wasacquired on weekly structured meeting (with alternating theme of journal club and problem based scenariopresentation) by a self reported questionnaire. The responses obtained on a 5-point Likert scale weredivided into two groups; I, senior faculty (professors, associates and assistants) II, junior faculty(lecturers). Chi square test was applied to compare categorical variables and results considered significantwith p value\u3c 0.05.Result: 49 faculty members; 15 in Group I and 34 in Group II responded, 90% respondent considered it tobe a healthy activity. Senior faculty agreed to the usefulness of structured meetings in terms of facultydevelopment, social interaction, provision of learning opportunities, upgrading of presentation,communication, listening and critical appraisal skills, understanding of biostatistics, self awareness,personal productivity and tolerance to listen to criticism more than the junior faculty (p-value 0.000).Conclusion: The perception regarding weekly structured meeting indicated that it enhanced faculty\u27sknowledge, improved presentation skills, enhanced confidence level, developed positive attitudes andpromoted educational leadership qualities in the faculty all through interaction and dialogue

A conglomeration of preclinical models related to myocardial infarction

Cardiovascular diseases are the main source of death and morbidity in developed and developing nations. Animal models are required to propel our understanding of the pathogenesis, increase our knowledge, disease progress, and mechanism behind cardiovascular disorder, providing new approaches focused to improve the diagnostic and the treatment of these pathological conditions and additionally to test various therapeutic ways to deal with tissue regeneration and re-establish heart working following damage. A perfect model framework ought to be reasonable, effectively controlled, reproducible, and physiologically illustrative of human disease, show cardinal signs and pathology that resembles after the human ailment and ethically stable. The decision of selection of animal model should be considered precisely since it influences exploratory results and whether results of the research can be sensibly matched with the human. In this way, no specific technique splendidly reproduces the human disease, and relying upon the model, extra cost burden, resources, infrastructure and the necessity for technical hands, should also be kept under consideration. Here we have discussed and compiled various methods of inducing myocardial infarction in animals, basically by surgery, chemicals and through genetic modification, this may benefit the researchers in getting a complied data regarding various methods through which they can induce myocardial infarction in animals

Antioxidant potential of crude extract, flavonoid-rich fractions, and a new compound from the seeds of Cordia dichotoma

The current study assessed the antioxidant activity of methanolic extract and different fractions of the seeds of Cordia dichotoma by 2,2-diphenyl-2-picrylhydrazyl hydrate method. Phytochemical screening of C. dichotoma seed extract was done using thin-layer chromatography technique and phytochemical methods. The percentage yield of secondary metabolites like alkaloids and saponins was also determined. The methanolic extract was subjected to isolation by Column Chromatography. Phytochemical screening revealed the presence of significant amounts of phenols and flavonoids in the extract. TLC analysis confirmed the presence of phytoconstituents with the application of derivatizing agents like aluminium chloride and anisaldehyde. Total phenolic and flavonoid contents obtained were 37.7 and 32.16% w/w, respectively. The crude seed extract of C. dichotoma showed inhibition at all concentrations in a dose-dependent manner. Maximum scavenging activity was exhibited by the methanolic extract with a low IC50 value. A new compound named Cordioside was also isolated from the same extract. The phytochemical screening of the seed extract showed the presence of rich amounts of phenolic compounds and flavonoids, which may be acting as the key factors responsible for the antioxidant activity. The results revealed that methanolic extract and the aqueous fraction of C. dichotoma seed possess a significant antioxidant activity

Antioxidant potential of crude extract, flavonoid-rich fractions, and a new compound from the seeds of Cordia dichotoma

437-44The current study assessed the antioxidant activity of methanolic extract and different fractions of the seeds of Cordia dichotoma by 2,2-diphenyl-2-picrylhydrazyl hydrate method. Phytochemical screening of C. dichotoma seed extract was done using thin-layer chromatography technique and phytochemical methods. The percentage yield of secondary metabolites like alkaloids and saponins was also determined. The methanolic extract was subjected to isolation by Column Chromatography. Phytochemical screening revealed the presence of significant amounts of phenols and flavonoids in the extract. TLC analysis confirmed the presence of phytoconstituents with the application of derivatizing agents like aluminium chloride and anisaldehyde. Total phenolic and flavonoid contents obtained were 37.7 and 32.16% w/w, respectively. The crude seed extract of C. dichotoma showed inhibition at all concentrations in a dose-dependent manner. Maximum scavenging activity was exhibited by the methanolic extract with a low IC50 value. A new compound named Cordioside was also isolated from the same extract. The phytochemical screening of the seed extract showed the presence of rich amounts of phenolic compounds and flavonoids, which may be acting as the key factors responsible for the antioxidant activity. The results revealed that methanolic extract and the aqueous fraction of C. dichotoma seed possess a significant antioxidant activity

LSTM based stock prediction using weighted and categorized financial news.

A significant correlation between financial news with stock market trends has been explored extensively. However, very little research has been conducted for stock prediction models that utilize news categories, weighted according to their relevance with the target stock. In this paper, we show that prediction accuracy can be enhanced by incorporating weighted news categories simultaneously into the prediction model. We suggest utilizing news categories associated with the structural hierarchy of the stock market: that is, news categories for the market, sector, and stock-related news. In this context, Long Short-Term Memory (LSTM) based Weighted and Categorized News Stock prediction model (WCN-LSTM) is proposed. The model incorporates news categories with their learned weights simultaneously. To enhance the effectiveness, sophisticated features are integrated into WCN-LSTM. These include, hybrid input, lexicon-based sentiment analysis, and deep learning to impose sequential learning. Experiments have been performed for the case of the Pakistan Stock Exchange (PSX) using different sentiment dictionaries and time steps. Accuracy and F1-score are used to evaluate the prediction model. We have analyzed the WCN-LSTM results thoroughly and identified that WCN-LSTM performs better than the baseline model. Moreover, the sentiment lexicon HIV4 along with time steps 3 and 7, optimized the prediction accuracy. We have conducted statistical analysis to quantitatively assess our findings. A qualitative comparison of WCN-LSTM with existing prediction models is also presented to highlight its superiority and novelty over its counterparts