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Maintaining software quality is a significant challenge as the complexity of software is increasing with the rise of the software industry. Software defects are a primary concern in complex modules, and predicting them in the early stages of the software development life cycle (SDLC) is difficult. Previous techniques to address this issue have not been very promising. We have proposed “A hybrid ensemble model for software defect prediction using AI‐based techniques with feature preservation” to overcome this problem. We have used the National Aeronautics and Space Administration (NASA) dataset from the PROMISE repository for testing and validation. By applying exploratory data analysis (EDA), feature engineering, scaling, and standardization, we found that the dataset is imbalanced, which can negatively affect the model's performance. To address this, we have used the Synthetic Minority Oversampling (SMOTE) technique and the edited nearest neighbor (ENN) (SMOTE‐ENN). We have also used recursive feature elimination cross‐validation (RFE‐CV) with a pipeline to prevent data leaking in CV and kernel‐based principal component analysis (K‐PCA) to minimize dimensionality and selectively relevant features. The reduced dimensional data is then given to the eXtreme Gradient Boosting (XGBoost) for classification, resulting in the hybrid‐ensemble (SMERKP‐XGB) model. The proposed SMERKP‐XGB model is better than previously developed models in terms of accuracy (CM1: 97.53%, PC1: 92.05%, and PC2: 97.45%, KC1:95.65%), and area under the receiver operating characteristic curve values (CM1:96.30%, PC1:98.30%, and PC2:99.30%: KC1: 93.54) and other evaluation criteria mentioned in the literature. [ABSTRACT FROM AUTHOR]

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC. [ABSTRACT FROM AUTHOR]

BACKGROUND: Terbutaline is the drug of choice for asthma patients but it exists in racemic mixture. (R)‐(−)‐terbutaline is 200 times more active than (S)‐(+)‐terbutaline yet it is not advisable to prescribe racemic mixture due to certain adverse effects of (S)‐(+)‐terbutaline. Therefore, a fast, effective and reproducible separation method is necessary. RESULTS: Chiral separation was achieved on Chiralpak‐IE and Chiralpak‐IG columns (250 × 4.6 mm, 5 μm) using carbon dioxide‐methanol (CO2‐MeOH, 60:40) with a 0.2% triethylamine mobile phase. The flow was 1.0 mL min−1 with detection at 223 nm using a photodiode array (PDA) detector. The values of retention, separation and resolution factors were in the ranges 1.88–2.38, 1.14–1.26 and 0.91–1.17, respectively; with best separation with Chiralpak‐IE. The tailing factors and number of theoretical plates were in the range of 1.0–1.23 and 487–3699. The purity of the separated peaks was determined by ultra‐performance liquid chromatography–mass spectrometry (UPLC–MS); indicating 100% purity of the peaks. The chiral recognition was determined by modeling with binding affinities −5.0 and −6.0 for S‐ and R‐enantiomers, respectively; indicating S‐enantiomer elution first followed by R‐enantiomers. The major forces responsible for the chiral resolution were hydrogen bonding and π‐π interactions. CONCLUSION: Owing to the great demand for optically active pure drugs and high economic pressure on analytical techniques, the chiral separation of terbutaline was achieved via inexpensive supercritical fluid chromatography. The reported method may be used to prepare optically active pure terbutaline drugs (R‐enantiomers) at a pilot scale. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

The extraction of berberine was carried out from Berberis vulgaris, Berberis aquifolium, and Hydrastis canadensis plants using ethanol and water (70:30, v/v). The extracted berberine was characterized by ultraviolet‐visible and Fourier‐transform infrared spectroscopy. The purity of berberine was ascertained by thin‐layer chromatography using n‐propanol‐formic acid‐water (95:1:4) and (90:1:9) solvents. hRf values were in the range of 44–49 with compact spots (diameter 0.2–0.4 cm). HPLC was carried out using ammonium acetate buffer and acetonitrile in gradient mode with Zodiac (4.6 × 150 mm, 3 μm) column. The flow rate was 1.0 mL/min and detection was at 220 nm. The values of separation and resolution factors of the standard and the extracted berberine were in the range of 1.13–1.16 and 1.40–1.71, respectively. A comparison has shown that both thin‐layer chromatography and high‐performance liquid chromatography (HPLC) methods found applications in different situations and requirements. The extracted berberine samples were used to treat Leishmaniosis and the results showed better activity of berberine in comparison to the standard drug Amphotericin B. Briefly, the reported research is a novel and may be used to extract berberine from plants, separation and identification of berberine by thin layer chromatography and HPLC and to treat Leishmaniosis. [ABSTRACT FROM AUTHOR]

In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research. [ABSTRACT FROM AUTHOR]