Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.     

Key exchange process of PIM-SM-based for Multiple Group Communication in P2P

Peer-to-Peer Networking and Applications - Recently, as the P2P service environment repeats evolution, its usage in mobile is rapidly increasing. Applications are being developed in various mobile...     

Bioactive compounds and antioxidant activities of the Korean lotus leaf (Nelumbo nucifera) condiment: volatile and nonvolatile metabolite profiling during fermentation

Recent studies have shown that lotus (Nelumbo nucifera) leaf has various pharmacological effects. However, there have been no scientific investigations into these leaves as a fermented crude liquid. This study compared the in vitro antioxidant capacity of lotus leaf‐fermented broth (LLFB) with that of 57° Brix sugar broth (SB) as a control over a period of 6 months based on 1,1‐diphenyl‐2‐picrylhydrazyl radical scavenging activity, ferric ion reducing power, superoxide dismutase‐like activity, tyrosinase inhibition and nitrite scavenging activity. The dominant species during fermentation process were Leuconostoc lactis and Saccharomyces cerevisiae for LLFB. A total of thirty‐six metabolites such as alkaloids, fatty acids, organic acids, phenolics, sugar and sugar derivatives, ethyl esters and monoterpenoids differed between SB and LLFB using gas chromatography–mass spectrometry. Meanwhile, nine volatile compounds in LLFB contributed pleasant, slightly sweetish and fruity odour of condiment and sensory evaluation score of 4.06 ± 1.52 in the proportion of 1:9 LLFB/water.     

Compensation paradox: the influence of monetary rewards on user behaviour

Many e-commerce companies collect users’ personal data for marketing purposes despite privacy concerns. Information-collecting companies often offer a monetary reward to users to alleviate privacy concerns and ease the collection of personal information. This study focused on the negative effects of monetary rewards on both information privacy concerns (IPC) and information disclosure. A survey approach was used to collect data and 370 final responses were analysed using a two-way analysis of variance and a binomial logistic regression model. The results show that monetary rewards increase IPC when an information-collecting company requires sensitive information. Additional results indicate that building trust is a more effective way of collecting personal data. This study identifies how organisations can best execute information-collection activities and contributes additional insights for academia and practitioners.