Towards a component-based system model to improve the quality of highly configurable systems.

Due to ever-evolving software developments processes, companies are motivated to develop desired quality products quickly and effectively. Industries are now focusing on the delivery of configurable systems to provide several services to a wide range of customers by making different configurations in a single largest system. Nowadays, component-based systems are highly demanded due to their capability of reusability and restructuring of existing components to develop new systems. Moreover, product line engineering is the major branch of the component-based system for developing a series of systems. Software product line engineering (SPLE) provides the ability to design several software modifications according to customer needs in a cost-effective manner. Researchers are trying to tailor the software product line (SPL) process that integrates agile development technologies to overcome the issues faced during the execution of the SPL process such as delay in product delivery, restriction to requirements change, and exhaustive initial planning. The selection of suitable components, the need for documentation, and tracing back the user requirements in the agile-integrated product line (APL) models still need to improve. Furthermore, configurable systems demand the selected features to be the least dependent. In this paper, a hybrid APL model, quality enhanced application product line engineering (QeAPLE) is proposed that provides support for highly configurable systems (HCS) by evaluating the dependency of features before making the final selection. It also has a documentation and requirement traceability function to ensure that the product meets the desired quality. Two-fold assessments are undertaken to validate the suggested model, with the proposed model being deployed on an active project. After that, we evaluated the proposed model performance and effectiveness using after implementing it in a real-world environment and compared the results with an existing method using statistical analysis. The results of the experimental study proofs that the proposed model is practically and statistically significant as compared to the existing method in terms of effectiveness and participants' performance. Hence, the statistical results of the comparative analysis show that the proposed model improved ease of understanding and adaptability, required effort, high-quality achievement, and version management are significant i.e., more the 50% as compared to the exiting method i.e., less than 50%. The proposed model offers to assist in the development of a highly configurable system that achieves the needed quality. Therefore, the proposed model manages the variation identification, versions control, components dependency for correct selection of components, and validation activities from domain engineering to application engineering.

Effective and Reliable Framework for Lung Nodules Detection from CT Scan Images.

Lung cancer is considered more serious among other prevailing cancer types. One of the reasons for it is that it is usually not diagnosed until it has spread and by that time it becomes very difficult to treat. Early detection of lung cancer can significantly increase the chances of survival of a cancer patient. An effective nodule detection system can play a key role in early detection of lung cancer thus increasing the chances of successful treatment. In this research work, we have proposed a novel classification framework for nodule classification. The framework consists of multiple phases that include image contrast enhancement, segmentation, optimal feature extraction, followed by employment of these features for training and testing of Support Vector Machine. We have empirically tested the efficacy of our technique by utilizing the well-known Lung Image Consortium Database (LIDC) dataset. The empirical results suggest that the technique is highly effective for reducing the false positive rates. We were able to receive an impressive sensitivity rate of 97.45%.

Anti-mycobacterial screening of five Indian medicinal plants and partial purification of active extracts of Cassia sophera and Urtica dioica.

OBJECTIVE: To find out the anti-mycobacterial potential of Cassia sophera (C. sophera), Urtica dioica (U. dioica), Momordica dioica, Tribulus terrestris and Coccinia indica plants against multi-drug resistant (MDR) strain of Mycobacterium tuberculosis (M. tuberculosis). METHODS: Plant materials were extracted successively with solvents of increasing polarity. Solvent extracts were screened for anti-mycobacterial activity against fast growing, non-pathogenic mycobacterium strain, Mycobacterium semegmatis, by disk diffusion method. The active extracts were tested against MDR and clinical isolates of M. tuberculosis by absolute concentration and proportion methods. The active extracts were subjected to bio-autoassay on TLC followed by silica column chromatography for isolation of potential drug leads. RESULTS: Hexane extract of U. dioica (HEUD) and methanol extract of C. sophera (MECS) produced inhibition zone of 20 mm in disc diffusion assay and MIC of 250 and 125 µ g/mL respectively in broth dilution assay against Mycobacterium semegmatis. Semipurified fraction F2 from MECS produced 86% inhibition against clinical isolate and 60% inhibition against MDR strain of M. tuberculosis. F18 from HEUD produced 81% inhibition against clinical isolate and 60% inhibition against MDR strain of M. tuberculosis. Phytochemical analysis indicated that anti-mycobacterial activity of MECS may be due to presence of alkaloids or flavonoids and that of HEUD due to terpenoids. CONCLUSIONS: C. sophera and U. dioica plant extracts exhibited promising anti-mycobacterial activity against MDR strain of M. tuberculosis. This is the first report of anti-mycobacterial activity form C. sophera. This study showed possibility of purifying novel anti-mycobacterial compound(s) from C. sophera and U. dioica.