An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.</p

Mobile-based Skin Lesions Classification Using Convolution Neural Network

This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas

Exploring Plant Genetic Variations with Morphometric and Molecular Markers

For centuries, crop improvement has served as the basis of food security of ever increasing human population. Though vast germplasm collections are available; their exploitation for crop improvement still depends upon efficient assessment of genetic diversity. Genetic variability is the key element in adaptation of plants to varying climates. While crops with narrow genetic diversity are vulnerable to stresses. The estimation of extent and pattern of genetic variability is a prerequisite for generating superior varieties. Genetic diversity analysis generates key information to dissect genetic variations in crop germplasm with the help of morphometrical, biochemical and molecular tools. Among these, DNA markers provide a reliable and detailed insight into the similarities and differences among crops. In this chapter, we discuss the applications of phenotypic and molecular markers to probe genetic divergence in crops and present case studies that describe the significance of these tools to characterize sorghum germplasm. Furthermore, we spotlight sorghum biodiversity exploration efforts worldwide and propose future directions

Achievements of neural network in skin lesions classification

The gross mismatch of skin disease cases and the specialties to manage them is the main cause of a continuously increased disease burden. The skin disease burden contributes 1.79% toward the global disease burden. To lessen this burden, automated skin lesions classification schemes that can provide multiclass classification are highly demanded. This chapter presents an investigation into an automated classification scheme to classify multiple skin lesions (acne, eczema, psoriasis; benign, and malignant) using state-of-the-art machine learning techniques. In the proposed classification scheme, convolution neural network (CNN) is utilized using the transfer learning approach, and a pretrained CNN model “AlexNet” is used to retrain the classification model on the skin lesion dataset. The proposed classification scheme outperformed over existing classification schemes and obtained an accuracy of 96.65%. The multiclass classification scheme can be very beneficial in the limited resource areas as it can assist in the early diagnosis of multiple skin lesions

Metabolomics: A Way Forward for Crop Improvement

Metabolomics is an emerging branch of &ldquo;omics&rdquo; and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment&ndash;gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics

Prunus armeniaca Gum-Alginate Polymeric Microspheres to Enhance the Bioavailability of Tramadol Hydrochloride: Formulation and Evaluation

International audienceCombinations of polymers can improve the functional properties of microspheres to achieve desired therapeutic goals. Hence, the present study aimed to formulate Prunus armeniaca gum (PAG) and sodium alginate microsphere for sustained drug release. Blended and coated microspheres were prepared using the ionotropic gelation technique. The effect of polymer concentration variation was studied on the structural and functional properties of formulated microspheres. FTIR, XRD, and thermal analysis were performed to characterize the microspheres. All the formulations were well-formed spherical beads having an average diameter from 579.23 +/- 07.09 to 657.67 +/- 08.74 mu m. Microspheres entrapped drugs within the range 65.86 +/- 0.26-83.74 +/- 0.79%. The pH-dependent swelling index of coated formulations was higher than blended. FTIR spectra confirmed the presence of characteristic peaks of entrapped Tramadol hydrochloride showing no drug-polymer interaction. In vitro drug release profile showed sustained release following the Korsmeyer-Peppas kinetic model with an R-2 value of 0.9803-0.9966. An acute toxicology study employing the oral route in Swiss albino mice showed no signs of toxicity. It can be inferred from these results that blending PAG with sodium alginate can enhance the stability of alginate microspheres and improve its drug release profile by prolonging the release time

The Impact of organizational culture and Training on Leadership Development

Purpose: The purpose of this research paper is the impact of organizational culture and training on leadership development and makes the detailed study of organizational culture and training in the context of leadership development. Design/methodology/approach: A mixed methods approach (e.g. both qualitative as well as quantitative) was utilized in order to carry out this study. The questionnaire consisted of 26 statements which make reference to the main functions of organizational leadership development, such as, Training and Organization Culture. The gap analysis method was used for the questionnaire items. Finally, complimentary to the questionnaires, it was decided to have on-site visitations with “real people” on the ground. Findings: The study shows that leadership training programs and organizational culture are tools which are used for Leadership development. Practical implication: This practical evidence provides guidelines for Human Resource researchers and managers for providing training and organizational culture which effect on leadership development Limitation of research: The major limitation of this research is cost, time and research culture. Originality/value: Leadership training programs and organizational culture need to be supplemented with Leadership development activities and a more systematic follow-up process after completion.