scholarly journals Modelling in Synthesis and Optimization of Active Vaccinal Components

Nanomaterials ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3001
Author(s):  
Oana-Constantina Margin ◽  
Eva-Henrietta Dulf ◽  
Teodora Mocan ◽  
Lucian Mocan
Keyword(s):  
Heart Diseases ◽  
Therapeutic Vaccine ◽  
Human Perception ◽  
Quantitative Structure Activity Relationship ◽  
Neural Nets ◽  
Molecular Characteristics ◽  
Network Building ◽  
Colon And Rectum ◽  
Morphological Transformations ◽  
Processing Techniques

Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.

scholarly journals Neutrosophy for physiological data compression: in particular by neural nets using deep learning

2020 ◽  
pp. 74-80
Author(s):  
Philippe Schweizer ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Boundary Surface ◽  
Human Perception ◽  
Neural Nets ◽  
Physiological Data ◽  
Compression Process ◽  
The Neural Network ◽  
Algorithmic Solution ◽  
Algorithmic Techniques

We would like to show the small distance in neutropsophy applications in sciences and humanities, has both finally consider as a terminal user a human. The pace of data production continues to grow, leading to increased needs for efficient storage and transmission. Indeed, the consumption of this information is preferably made on mobile terminals using connections invoiced to the user and having only reduced storage capacities. Deep learning neural networks have recently exceeded the compression rates of algorithmic techniques for text. We believe that they can also significantly challenge classical methods for both audio and visual data (images and videos). To obtain the best physiological compression, i.e. the highest compression ratio because it comes closest to the specificity of human perception, we propose using a neutrosophical representation of the information for the entire compression-decompression cycle. Such a representation consists for each elementary information to add to it a simple neutrosophical number which informs the neural network about its characteristics relative to compression during this treatment. Such a neutrosophical number is in fact a triplet (t,i,f) representing here the belonging of the element to the three constituent components of information in compression; 1° t = the true significant part to be preserved, 2° i = the inderterminated redundant part or noise to be eliminated in compression and 3° f = the false artifacts being produced in the compression process (to be compensated). The complexity of human perception and the subtle niches of its defects that one seeks to exploit requires a detailed and complex mapping that a neural network can produce better than any other algorithmic solution, and networks with deep learning have proven their ability to produce a detailed boundary surface in classifiers.

Detection of Blood-Related Diseases Using Deep Neural Nets

2019 ◽  
pp. 1-15
Author(s):  
Rajithkumar B. K. ◽  
Shilpa D. R. ◽  
Uma B. V.
Keyword(s):  
Image Processing ◽  
Biomedical Engineering ◽  
Medical Diagnosis ◽  
Blood Smear ◽  
Neural Nets ◽  
Laboratory Methods ◽  
Intelligent Algorithms ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

scholarly journals Instance Segmentation on Real time Object Detection using Mask R-CNN

2019 ◽  
Vol 9 (1) ◽  
pp. 2932-2935
Keyword(s):  
Image Processing ◽  
Search Algorithm ◽  
Human Interaction ◽  
Unique Object ◽  
Detection Of Objects ◽  
Morphological Transformations ◽  
Processing Techniques ◽  
Instance Segmentation ◽  
Image Mask ◽  
Real World Problems

In the ever-advancing field of computer vision, image processing plays a prominent role. We can extend the applications of Image processing into solving real-world problems like substantially decreasing Human interaction over the art of driving. In the process of achieving this task, we face several challenges like Segmentation and Detection of objects. The proposed thesis overcomes the challenges effectively by introducing Instance segmentation and Binary masks along with Keras and Tensorflow. Instance segmentation is used to delineate and detect every unique object of interest according to their pixel characteristics in an image. Mask RCNN is the superior model over the existing CNN models and yields accurate detection of objects more efficiently. Unlike conventional Neural Networks which employs selective search algorithm to identify object of interest, Mask RCNN employs Regional Proposal Networks(RPN) to identify object of interest. For better results Image pre-processing techniques and morphological transformations are employed to reduce the noise and increase pixel clarity

Detection of Heart Defects using Electrocardiogram (ECG)

2018 ◽  
Vol 4 (11) ◽  
Author(s):  
Kenil Shah ◽  
Mayur Rane ◽  
Dr. Vahid Emamian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Heart Diseases ◽  
Heart Defects ◽  
Healthy Person ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Processing Techniques ◽  
Electrocardiogram Ecg ◽  
Ecg Data

Electrocardiogram (ECG) signals are vital to identifying cardiovascular disease. The numerous availability of signal processing and neural networks techniques for processing of ECG signals has inspired us to do research on extracting features of ECG signals to identify different cardiovascular diseases. We distinguish between a healthy person ECG data and person having disease ECG data using signal processing and neural network toolbox in Matlab. The data was downloaded from physiobank. To distinguish normal and abnormal ECG, Neural network is used. Feature extraction method is used to identify heart diseases. The diseases that are identified include Tachycardia, Bradycardia, first- degree Atrioventricular (AV) and a healthy person. Subsequently, ECG signals are very noisy; signal processing techniques are used to remove the noise impurity. The heart rate can be calculated by detecting the distance between R-R intervals of the signal. The algorithm successfully distinguished between normal and abnormal ECG data.

Demographic, clinicopathologic and survival differences among left sided versus right sided metastatic colorectal adenocarcinoma.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15068-e15068
Author(s):  
Smith Giri ◽  
Kathan Mehta ◽  
Shreesh Shrestha ◽  
Santosh Sharma ◽  
Vijaya Raj Bhatt
Keyword(s):  
African Americans ◽  
Lymphovascular Invasion ◽  
Tumor Biology ◽  
Stage Iv ◽  
Hepatic Flexure ◽  
Molecular Characteristics ◽  
P Value ◽  
Pathologic Features ◽  
Colon And Rectum ◽  
Grade 3

e15068 Background: Clinical outcomes may differ between left versus right sided (CA) independent of stage. We utilized a large database to identify real world differences in characteristics and overall survival (OS) of left versus right sided CA. Methods:We used the National Cancer Database (NCDB) to identify 36,271 patients with metastatic (stage IV) CA diagnosed between the years 2010 to 2012. Right-sided CA was defined as CA of the cecum, ascending colon or hepatic flexure, and left-sided CA was defined as CA of splenic flexure, descending colon, sigmoid colon and rectum. We compared demographic and pathologic features (tumor size, grade, lymphovascular invasion, metastatic site) as well as OS of CA based on laterality. Results:36% of metastatic CAs were right sided. Left versus right sided CAs were more likely to be diagnosed in older patients (median age 68 vs. 62 yr; p<0.01), females (52% vs. 43%; p<0.01), and African Americans (17% vs. 13%; p <0.01). Left-sided CAs were more likely to be >5 cm (58% vs. 54%, p<0.01), grade 3 or 4 (35% vs. 22%; p<0.01), have lymphovascular invasion (62% vs. 50%; p <0.01) and liver metastasis (72% vs 67%; p<0.01) and less likely to have lung (20% vs. 27%;p <0.01) or bone metastasis (5% vs. 6%; p value <0.01). The median OS was lower in right versus left-sided CAs(12 vs. 20 months; p <0.01) Conclusions:Our study suggests important differences in metastatic CAs based on laterality. Patients with right-sided CAs are more likely to be older, females and African Americans, present with larger tumor, lymphovascular invasion and differ in sites of metastases. Future studies should assess any difference in molecular characteristics and tumor biology based on laterality. [Table: see text]

scholarly journals Rice Grain Quality Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 262-267
Author(s):  
V. Vaishnavi
Keyword(s):  
Human Perception ◽  
Physical Characteristics ◽  
Rice Grain ◽  
Human Beings ◽  
Rice Grains ◽  
Standard Quality ◽  
Grain Size And Shape ◽  
Processing Techniques ◽  
Physical And Chemical

The quality of grain is of great importance for human beings as it directly impacts human health. Hence there is a great need to measure the quality of grain and identifying non-quality elements. Analysing the grain samples manually is a more time-consuming and complicated process, and having more chances of errors with the subjectivity of human perception. To achieve uniform standard quality and precision, machine vision-based techniques are evolved. Rice quality is nothing but a combination of physical and chemical characteristics. So, to get the physical characteristics of the rice grains, image processing techniques are applied. Grain size and shape are some physical characteristics. The obtained all physical features grades the rice grains using canny edge detection.

Abrasion Measurement of Spun Yarns by Image Analysis and Artificial Intelligence Techniques

2014 ◽  
Vol 18 (3) ◽  
pp. 61-68
Author(s):  
D. Semnani
Keyword(s):  
Research Work ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The Body ◽  
Neural Nets ◽  
Image Processing Techniques ◽  
New Methods ◽  
Spun Yarns ◽  
Processing Techniques ◽  
First Time

Previously, to evaluate the abrasion of spun yarns, ASTM standard D1379-64 (1970) was applied and valid until 1975. After that, much research work has been carried out to study the abrasion resistance of yarns by using different methods. Recently, new methods based on image processing techniques have been developed. In this research, first, to calculate the abrasion indexes for an image of yarns that are wrapped side by side, the inputs for a back propagation neural network are provided and abrasion destruction indexes are the output. The training of the net is done with data from model images. Moreover, the network has been tested with those model images. To design the model images, attempts are made to simulate various types of defects which are made by abrasion on the body of yarn. After that, groups of spun and filament yarns are tested with both a standard and the new intelligent method and the results are compared. The results prove that trained neural nets have the ability to evaluate the images of yarns trained to the net before; in addition, they can evaluate the images which are inserted into the net for the first time.

Quantitative Structure-Activity Relationship between Compound Molecular Characteristics and Nanofiltration Separation Efficiency

2010 ◽  
Vol 168-170 ◽  
pp. 1185-1188
Author(s):  
Qing Yang ◽  
Xin Qiu
Keyword(s):  
Separation Efficiency ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Molar Refraction ◽  
Activity Relationship ◽  
Molecular Characteristics ◽  
Dodecanoic Acid ◽  
Quantitative Structure ◽  
Feed Concentration ◽  
Structure Activity

The aim of this study is to establish a certain Quantitative Structure-Activity Relationship (QSAR) between compound molecular characteristics and nanofiltration (NF) separation efficiency. Measurements were carried out in a crossflow NF unit and using ten organic compounds (ethanol, butyl alcohol, glycerin, phenol, glucose, sorbitolum, dodecanoic acid, Imidacloprid, sucrose and Dimethomorph) in aqueous solution and two commercial NF membranes (DK and NF90). Four kind compound characteristics of Molecular weight (Mw), Octanol-Water Partition Coefficient (logP), Molar Refraction (CMR), Henry’s law (H) are selected. Through regression, F test and t test, QSAR analysis was accomplished to prove the validity of regression equation with confidence probability of equation coefficient above 85%. It could be concluded that Mw contributed most to rejection of DK and NF90 according to QSAR at constant flux (500mg/L) and feed concentration (500mg/L). The contribution of CMR is less than MW for NF90 rejection, following by logP, H.

scholarly journals Flower Species Detection Over Large Dataset Using Convolution Neural Networks and Image Processing Techniques

2020 ◽  
Vol 5 (8) ◽  
pp. 1722-1727
Author(s):  
Simriti Koul ◽  
Udit Singhania
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Digital Camera ◽  
Neural Nets ◽  
Learning Approach ◽  
Query Image ◽  
Species Detection ◽  
Image Processing Techniques ◽  
Optimization Parameters ◽  
Processing Techniques

We investigate flower species detection on a large number of classes. In this paper, we try to classify flower species using 102 flower species dataset offered by Oxford. Modern search engines provide methods to visually search for a query image that contains a flower, but it lacks robustness because of the intra-class variation among millions of flower species around the world. So, we use a Deep learning approach using Convolutional Neural Networks (CNN) to recognize flower species with high accuracy. We use the Oxford dataset which was made by the use of electronic items like a built-in camera in mobile phones and also a digital camera. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). We also use image augmentation and image processing techniques to extract the flower images more efficiently. After the experimental analysis and using different pre-trained models, we achieve an accuracy of 85%. Further advancements can be made by using optimization parameters in the neural nets.

scholarly journals Image Processing & Neural Network Based Breast Cancer Detection

2019 ◽  
Vol 12 (2) ◽  
pp. 146
Author(s):  
Marwan Abo Zanona
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Cancer Patients ◽  
Human Perception ◽  
Discrete Wavelet ◽  
Breast Cancer Patients ◽  
Input Material ◽  
Processing Techniques ◽  
Mammogram Images

A large percentage of cancer patients are breast cancer patients. The main available methodology to examine the breast cancer is the Mammography. It detects the signs of breast cancer as different signs supports the experts&rsquo; decision. Actually, the Mammography is based on human perception and observations. So, build an AI computerized system will take major role in early signs detection. This paper presents an image processing with aid of artificial neural networks computations for computerized signs detection and exploration of breast cancer. The input material is the mammogram images, and the output helps the pathologists to take a decision. A set of input mammogram images was used for development, testing, and evaluation. The mammographic image will be preprocessed and then the features will be extracted using discrete wavelet transformation with aid of Weiner filtration. A historical data of extracted features were used to train a neural network, while the historical extracted features contains both Cancer and non-Cancer images. The combination of neural network machine learning, and rigid image processing techniques resulted accurate outputs. The methodology and results are showed and discussed later in this paper.

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