Volume 9 | Issue 2 |

 Abstract

Bio-synthesis of nanoparticles is gaining huge importance, these days. Gold nanoparticles (GNPs) are widely used for various applications such as drug delivery, bioprobe, Imaging and drug targeting. Various sources such as plant extracts, fungi and microbes are used for the synthesis of GNPs. Algae are living in a sessile aquatic environment for its survival and it has certain bioactive compounds such as carbohydrates, aminoacids, proteins, fatty acids, flavonoids, antioxidants, saponins, etc. However, there are only limited studies for the synthesis of GNPs using algae. Hence, in this study, clinically potent GNPs were synthesized using fresh water green algae Charavulgaris. In the present study, the colour of the prepared solution changes from yellowish green to red wine indicates the synthesis of gold nanoparticles. In addition, UV-Vis spectrophotometer, SEM, XRD and FT-IR analysis was done for the analyses of gold nanoparticles. Furthermore, antibacterial study was done and it showed maximum sensitivity to the given bacterial species.

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 Keywords

Bio synthesis, gold nanoparticles, fresh water algae, Charavulgaris, anti-microbial study

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 Abstract

Detecting and classifying fetal brain abnormalities from magnetic resonance imaging (MRI) is important, as approximately 3 in 1000 women are pregnant with a fetal of abnormal brain. Early detection of fetal brain abnormalities using machine learning techniques can improve the quality of diagnosis and treatment planning. The literature has shown that most of the work made to classify brain abnormalities in a very early age is for preterm infants and neonates not fetuses. However, research papers that studied fetal brain MRI images have mapped these images with the neonates MRI images to classify an abnormal behaviour in newborns not fetal. In this work, a pipeline process is proposed for fetal brain classification (FBC) which uses machine learning techniques. The main contribution of this work is the classification of fetal brain abnormalities in early stage, before the fetal is born. The proposed algorithm is capable of detecting and classifying a variety of abnormalities from MRI images with a wide range of fetal gestational age (GA) (from 16 to 39 weeks) using a flexible and simple method with low computational cost. The novel proposed method consists of four phases; pre-processing, segmentation, feature extraction and classification. In the pre-process, the input image is converted into gray scale and apply the wiener filter for image enhancement. After pre-processing, the adaptive segmentation is applied for segmenting the image. Then DWT and statistical features are extracted from segmented images. Finally, the input image is classified using the Decision Tree algorithm.

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 Keywords

Fetal Brain Abnormalities, machine learning algorithms

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 Abstract

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 Keywords

SVD, GLCM, certificate authentication

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 Abstract

Diabetes occurs when the pancreas fails to secrete enough insulin, slowly affecting the retina of the human eye. As it progresses, the vision of a patient starts deteriorating, leading to diabetic retinopathy. In this regard, retinal images are collected from DRIVE dataset images which are analyzing the consequences, nature, and status of the effect of diabetes on the eye. The objectives of this work are to (i) detect blood vessel, (ii) identify hemorrhages and (iii) classify different stages of diabetic retinopathy into normal or abnormal. The basis of the classification of different stages of diabetic retinopathy is the detection and quantification of blood vessels and hemorrhages present in the retinal image. Retinal vascular is segmented utilizing the contrast between the blood vessels and surrounding background. Hemorrhage candidates were detected using density analysis and bounding box techniques. Finally, classification of the different stages of eye disease was done using Random Forests technique based on the GLCM and LBP features of the blood vessels and hemorrhages. The proposed methodology for detection and grading of Diabetic Retinopathy is divided into following stages such as preprocessing, Optic Disc Removal, Blood Vessel Segmentation and Removal, Features Extraction and classification.

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 Keywords

Diabetic retinopathy detection, LBP, GLCM

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 Abstract

Dengue fever is a major problem in many developing countries, including India. For dengue patient monitoring, platelet count is vital to ensure early treatment in order to prevent disease complications. In primary health care centers platelet counting is typically performed manually, which is labor intensive and requires an experienced laboratory technician. Another method used, is the Advia hematology analyzer, which is very expensive, not affordable for rural and remote areas. To address present day challenges, developed an automated approach for the detection of platelet, along with the symptoms helps in assisting the detection of dengue. The technology is based on microscopic images derived from blood smears obtained using a digitalized camera attached to a microscope. Image processing and segmentation techniques are applied to estimate the platelet count from these blood slides. To further improve the accuracy of the results, an analysis of symptoms present in the patient is used in conjunction with the platelet analysis. Proposed vector based method for screening the samples. The classifier performance is evaluated with the sensitivity and specificity values. The results of platelet counts obtained from other platelet counting machines and manually are compared. The reported tool helps in the modernization of pathology laboratory into digital, as well it speedup the mass screening.

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Dengue fever prediction, machine learning algorithms

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 Abstract

Lung cancer is considered to be the one among the most dreaded disease which will be the main reason for the death of individuals and having greater deterioration of death if it is not identified at primitive stage. Because of the fact that Lung cancer could be identified only after spreading to the parts of lungs to a greater extent and it is very tough to predict the presence of lung cancer at the earlier stage. Moreover, it involves greater error in the diagnosing the presence of Lung cancer by Radiologists and Expert Doctors. Therefor it is compulsory to design an intelligent and automated system for accurately predicting the cancer and stage at which the stage of cancer or enhancing the accuracy of prediction for detecting the cancer at earlier which will be much helpful in deciding the treatment type and depth of the treatment based on the extent of disease. Here this work establishes the idea of preprocessing which used correlation ranking algorithm and Adaboost Algorithm in the classification of lung cancer and its stages and the predicting the possibility of recurrence.

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Adaboost algorithm, Lung Cancer prediction

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Heart block diseases are one of the most deadly diseases with a high mortality rate. The shape and size of the block are random during the growth process. Heart Block segmentation is a heart block assisted diagnosis technology that separates different block structures such as edema and active and block necrosis tissues from normal heart tissue. Magnetic resonance imaging (MRI) technology has the advantages of no radiation impact on the human body, good imaging effect on structural tissues, and an ability to realize tomographic imaging of any orientation. Therefore, doctors often use MRI heart block images to analyze and process heart blocks. In these images, the block structure is only characterized by gray scale changes, and the developed images obtained by different equipment and different conditions may also be different. This makes it difficult for traditional image segmentation methods to deal well with the segmentation of heart block images. Considering that the traditional single-mode MRI heart block images contain incomplete heart block information, it is difficult to segment the single-mode heart block images to meet clinical needs. In this work, an automatic thresolding algorithm is introduced to process the diagnosis of MRI heart block images. In the absence of added noise, the proposed algorithm has better advantages than traditional methods. Experimental results show that the proposed algorithm has better noise immunity than a comparable algorithm.

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Image processing, feature extractions

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 Abstract

According to the World Health Organization (WHO), the corona virus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML), there had been intelligent systems to classify between Pneumonia and Normal patients. This work proposes the machine learning-based logistic regression classification algorithm. Bioinformatics and genomic signal processing use computational techniques to solve various biological problems. They aim to study the information allied with genetic materials such as the Deoxyribonucleic Acid (DNA), the Ribonucleic acid (RNA), and the proteins. Fast and precise identification of the protein coding regions in DNA sequence is one of the most important tasks in analysis. Existing Digital Signal Processing (DSP) methods provide less accurate and computationally complex solution with greater background noise. Hence, improvements in accuracy, computational complexity, and reduction in background noise are essential in identification of the protein-coding regions in the DNA sequences. In this work, a new DSP based method is introduced to detect the protein coding regions in DNA sequences. Here, the DNA sequences are converted into numeric sequences using electron ion interaction potential (EIIP) representation. In this work, we use DNA sequence of the coronavirus. The test is conducted using the databases available in the National Centre for Biotechnology Information (NCBI) site.

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Corona DNA sequences

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 Abstract

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Introduction, Knowledge Society, organization of Knowledge, objectives, Basic Principles, knowledge centers

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Abstract: The fossil fuel engines which were good enough for us before 30-40 years but now they are one of the sources of contributor of global warming and pollution with fossil fuel crises. The Air Powered Vehicle is an eco-friendly vehicle which works on compressed air. An Air Powered vehicle uses air as a fuel. An Air Powered Vehicle uses the expansion of compressed air to drive the pistons of an engine. An Air Driven Engine is a pneumatic actuator that creates useful work by expanding compressed air. There is no mixing of fuel with air as there is no combustion. In this project, we are using a 5/2 solenoid valve connected to a flasher which is connected to a double acting pneumatic cylinder which is in turn connected to a modified piston head which rotates the flywheel and moves the engine forward thus creating motion.

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 Keywords

Air Powered Vehicle, 5/2 Solenoid Valve, Double Acting Pneumatic Cylinder.

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