Advanced non-dimensional dynamic influence function method considering the singularity of the system matrix for accurate eigenvalue analysis of membranes

An advanced non-dimensional dynamic influence function method (NDIF method) for highly accurate free vibration analysis of membranes with arbitrary shapes is proposed in this paper. The existing NDIF method has the weakness of not offering eigenvalues and eigenmodes in the low frequency range when the number of boundary nodes of an analyzed membrane is increased to obtain more accurate result. This paper reveals that the system matrix of the membrane becomes singular in the lower frequency range when the number of the nodes increases excessively. Based on this fact, it provides an efficient way to successfully overcome the weaknesses of the existing NDIF method and still maintain its accuracy. Finally, verification examples show the validity and accuracy of the advanced NDIF method proposed.

Impact of Budget on Indian Share Market

This paper seeks to study the impact of budget on share market. The method of study was descriptive research. To study the impact is measured in average return by using the event window of Pre-Budget and Post-Budget of 30 Days and the data has been collected for the 18thDecember 2020 to 1stFebruary 2021 &2ndFebruary 2021 to 16thMarch 2021 (excluding Saturday, Sunday and Festival) and the statistical tools used are T-Test Paired, two sample for means on return are calculated by using the formula. The most probably there is not impact on budget on share market because at the same time there is other factors are also present in the market. The implication of this paper is that the investor should not only consider one factor and one event window because there is other factor present with affect the share price at the same time and by using the more than one event window. so, investor come to know a which period they will get a more returns so investor should use either one factor and multiple event window or take multiple factor and one event window. It gives more clear and accurate result. Company Name & Return Pre-Budget Budget-Day Post-Budget XYZ X1 X X2 Here we have taken the 10 companies return of pre-budget and post- budget. On the basis of this we will further analyse that investor should invest in which companies.

Development of Novel Method for the Estimation of Total Cholesterol in Chicken Eggs

Background: Cholesterol estimation in eggs was mostly done by High performance liquid chromatography (HPLC) and gas chromatography which are tedious, expensive and time consuming methods. Thus, there is a need to determine cholesterol content by an easy spectrophotometric method which is not expensive and tedious. The present study was aimed to determine the cholesterol content in chicken eggs spectrophotometrically so as to provide better information about the nutritional quality of an egg. Methods: The experiment was conducted on 24 healthy layer birds of Dahlem Red (DR). 45 egg samples were randomly collected fresh in the morning. Total cholesterol was determined by enzymatic colorimetric method by using biochemical estimation kit (Agappe Diagnostics Ltd.). The pure yolk was separated and determined enzymatically for cholesterol estimation. The method is newly evaluated, rapid, simple and accurate. Result: The methodology designed herein can provide specific, accurate and suitable method for estimation of total cholesterol in chicken eggs. The average cholesterol content of Dahlem red was 12.59±0.09 mg/ml in egg yolk or 195.66±2.80 mg/egg yolk.

Research on influence of mesh parameters modification on solution accuracy of finite element analysis

The paper deals with the solution accuracy of the stress in the structure using the finite element analysis. In general, hexahedron elements are more accurate than tetrahedron elements and quadratic elements are more accurate than linear elements. The primary aim of the article is to perform comparison of the obtained results and calculation parameters (such as time and so on) for different types of elements and the elements size. Usage less accurate elements like linear tetrahedron under certain circumstances could lead to sufficiently accurate result of stress analysis.

Single Image De-Raining using Supervised CNN Model

An image captured in rain reduces the visibility quality of image which affects the analytical task like detecting objects and classifying pictures. Hence, image de-raining became important in last few years. Since pictures taken in rain include rain streaks of all sizes, single image de-raining is becoming much difficult issue to solve, which may flow in different direction and the density of each rain streak is different. Rain streaks have a varied effect on various areas of picture, and hence it becomes important for removing rain streak from rainy pictures as rainy images tend to lose its high frequency information; previously many methods were proposed for this purpose but they failed to provide accurate results. Hence we have studied and implemented a supervised machine learning method using convolutional neural network (CNN) algorithm to get more accurate result of rain streak removal from an image captured during rain and in less elapsed time by preserving high rated information of image during removal of rain streak. Keywords: CNN, elapsed time, single image de-raining, supervised machine learning, rain streaks.

Diagnosis of Melanoma Lesion Using Neutrosophic and Deep Learning

Melanoma is a kind of skin cancer which occurs due to too much exposure of melanocyte cells to the dangerous UV radiations, that gets damaged and multiplies uncontrollably. This is popularly known as malignant melanoma and is comparatively less heard of than certain other types of skin cancers; however it can be more detrimental as it swiftly spreads if not detected and attended at a primary stage. The differentiation between benign and melanocytic lesions sometimes may be confusing, but the symptoms of the disease can reasonably be discriminated by a profound investigation of its histopathological and clinical characteristics. In the recent past, Deep Convolutional Neural Networks (DCNNs) have advanced in accomplishing far better results. The necessity of the present day is to have faster and computationally efficient mechanisms for diagnosis of the deadly disease. This paper makes an effort to showcase a deep learning-based ‘Keras’ algorithm, which is established on the implementation of DCNNs to investigate melanoma from dermoscopic and digital pictures and provide swifter and more accurate result as contrasted to standard CNNs. The main highlight of this paper, basically stands in its incorporation of certain ambitious notions like the segmentation performed by a culmination of a moving straight line with a sequence of points and the application of the concept of triangular neutrosophic number based on uncertain parameters. The experiment was done on a total of 40,676 images obtained from four commonly available datasets— International Symposium on Biomedical Imaging (ISBI) 2017, International Skin Imaging Collaboration (ISIC) 2018, ISIC 2019 and ISIC 2020 and the end result received was indeed motivating. It attained a Jac score of 86.81% on ISIC 2020 dataset and 95.98%, 95.66% and 94.42% on ISBI 2017, ISIC 2018 and ISIC 2019 datasets, respectively. The present research yielded phenomenal output in most instances in comparison to the pre-defined parameters with the similar types of works in this field.

ANN-based diagnosis method for skin cancers using dermoscopic images

Melanoma is one of the most dangerous skin cancers in the world. It accounts for 55% of all deaths associated with skin cancer. Researchers believe that skin cancer increases the risk of other cancers if not diagnosed early. Therefore, prompt and timely diagnosis of this disease is very important for the successful treatment of the patient. This system can detect melanoma lethal carcinoma from other skin lesions without the need for surgery, with a low cost, accuracy of about 98.88% and specificity 99%. In this article, a new, intelligent and accurate software (Delphi) system has been used to diagnose melanoma skin cancer. To detect malignant melanoma, the ABCDT rule, asymmetry (A), boundary (B), color (C), diameter (D) and textural variation (T) of the lesion are calculated and finally, an artificial neural network (ANN) is used to obtain an accurate result. The ANN with Multi-Layer Perceptron (MLP) contains the five extraction Characteristics (ABCDT) of lesions is used as inputs, two hidden layers, and two outputs. Very good results were obtained using this method. It was observed that for a dataset of 180 dermoscopic lesion images including 80 malignant melanomas, 20 benign melanomas and 80 nevus lesions. Due to its automatic recognition and ability to be installed on a computer, this system can be very useful for dermatologists as well as the general public.

Clinical assessment and imaging of the upper limb

Clinical assessment of the upper limb and hand requires a detailed history of the symptoms, or, in cases of trauma, the circumstances of the injury. Examination should be systematic and guided by a thorough knowledge of anatomy and the likely pathology. Various modalities of imaging can be applied to confirm or characterize the pathology. Each has particular indications and should be applied carefully so as to obtain the most accurate result.

MACHINE LEARNING BASED SEARCH ENGINE WITH CRAWLING, INDEXING AND RANKING

The web is the heap and huge collection of wellspring of data. The Search Engine are used for retrieving the information from World Wide Web (WWW). Search Engines are helpful for searching user keywords and provide the accurate result in fraction of seconds. This paper proposed Machine Learning based search engine which will give more relevant user searches in the form of web pages. To display the user entered query search engine plays a major role of basic interface. Every site comprises of the heaps of site pages that are being made and sent on the server.

Cloud Based Application for Prediction of Natural Disasters

Natural disasters are very dangerous and occurs common throughout the world, Natural disasters often result in injuries, damages and the other physical & mental health effects in India, detection has been one of the most active research in remote sensing today because of saving human life is our priority once a disaster occurred. Disaster leads to a great damage to the society. Artificial Intelligence can be used to analysis the data which can be used in prediction of warning for future events & create awareness for the situation. In Machine learning concept of random forest regression is used so that it can predict accurate result compare to other modules based on the result we have proposed the model for natural disasters detection to early saving life for humans/animals