Background: This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach.Discussion: The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging. This survey identifies the ML approach with better accuracy for medical diagnosis by radiologists. The image denoising approaches further includes basic filtering techniques, wavelet medical denoising, curvelet and optimization techniques. In most of the applications, the machine learning performance is better than the conventional image denoising techniques. For fast and computational results the radiologists are using the machine learning methods on MRI, US, X-Ray and Skin lesion images. The characteristics and contributions of different ML approaches are considered in this paper.Conclusion: The problem faced by the researchers during image denoising techniques and machine learning applications for clinical settings have also been discussed.

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

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Context:The identification of living or deceased persons using unique traits and characteristics of the teeth and jaws is a cornerstone of forensic science. Teeth have been used to estimate age both in the young and old, as well as in the living and dead. Gradual structural changes in teeth throughout life are the basis for age estimation. Tooth cementum annulation (TCA) is a microscopic method for the determination of an individual's age based on the analysis of incremental lines of cementum.Aim:To compare ages estimated using incremental lines of cementum as visualized by bright field microscopy, polarized microscopy, and phase contrast microscopy with the actual age of subject and to determine accuracy and feasibility of the method used.Materials and Methods:Cementum annulations of 60 permanent teeth were analyzed after longitudinal ground sections were made in the mesiodistal plane. The incremental lines were counted manually using a light, polarized and phase contrast microscopy. Ages were estimated and then compared with the actual age of individual.Statistical Analysis:Analysis of variance (ANOVA), Student's t-test, the Pearson product-moment corre (PPMCC) and regression analysis were performed.Results:PPMCC value r = 0.347, 0.542 and 0.989 were obtained using light, polarized and phase contrast microscopy methods respectively.Conclusion:It was concluded that incremental lines of cementum were most clearly visible under a phase contrast microscope, followed by a polarized microscope, and then a light microscope when used for age estimation.

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The growing environmental concerns due to the excessive use of non-renewable petroleum based products have raised interest for the sustainable synthesis of bio-based value added products and chemicals. Recently, nanocellulose has attracted wide attention because of its unique properties such as high surface area, tunable surface chemistry, excellent mechanical strength, biodegradability and renewable nature. It serves wide range of applications in paper making, biosensor, hydrogel and aerogel synthesis, water purification, biomedical industry and food industry. Variations in selection of source, processing technique and subsequent chemical modifications influence the size, morphology, and other characteristics of nanocellulose and ultimately their area of application. The current review is focused on extraction/synthesis of nanocellulose from different sources such as bacteria and lignocellulosic biomass, by using various production techniques ranging from traditional harsh chemicals to green methods. Further, the challenges in nanocellulose production, physio-chemical properties and applications are discussed with future opportunities. Finally, the sustainability of nanocellulose product as well as processes is reviewed by taking a systems view. The impact of chemicals, energy use, and waste generated can often negate the benefit of a bio-based product. These issues are evaluated and future research needs are identified.