scholarly journals Identifying Cancer Characteristics Utilizing Handwriting Method

2021 ◽  
Vol 12 (1S) ◽  
pp. 40-45
Author(s):  
S. V. Kedar, Et. al.
Keyword(s):  
Machine Learning ◽  
Vegetable Intake ◽  
High Body Mass Index ◽  
Handwriting Analysis ◽  
Machine Learning Methods ◽  
Handwritten Text ◽  
Motion Time ◽  
Mental Problems ◽  
The Brain ◽  
Behavioural Risks

Handwriting is an action administered by the brain like each and every other action. This procedure is frequently insensible and is closely tied to instincts from brain. Any kind of sickness affects the kinetic movement and reflects in a person’s handwriting. To recognize the health and mental problems, it is important to focus on how the person writes instead of what person writes. This also makes the procedure of handwriting analysis is independent of at all languages. Person handwriting is scientific proof that whatsoever person writes subconsciously it affects in handwriting. The structures related to motion, time and pressure have been used for analysis of person health. Cancer is the second top cause of death globally, and is accountable for an estimated 9.8 million deaths in 2019. Universally, around 1 in 6 deaths is due to cancer. On an approximation 72% of deaths due to cancer are in middle and low salaried countries. One third deaths from cancer are due to 5 foremost dietary and behavioural risks that are low fruit and vegetable intake, lack of physical activity, high body mass index, tobacco use, and consumption of alcohol. Cancer can be cured if the person gets to know as soon as possible. So, substitute method to patterned whether the person is diagnosed from a cancer or not, can be done by handwriting sample. For this testing 100 various person sample are used for diverse handwriting data samples. To find a solution to this mounting problem we propose the method of cancer characteristics detection by utilizing handwritten text by machine learning, SVM. Various machine learning methods were used to find a model, which can discriminate statistically Cancer patients with approximately 90%accuracy. The classification we use to discriminate are SVM, Naïve Bayes algorithms.

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

scholarly journals Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison

2021 ◽  
Vol 11 (6) ◽  
pp. 735
Author(s):  
Ilinka Ivanoska ◽  
Kire Trivodaliev ◽  
Slobodan Kalajdziski ◽  
Massimiliano Zanin
Keyword(s):  
Machine Learning ◽  
Computational Cost ◽  
Learning Task ◽  
Functional Networks ◽  
Large Set ◽  
Selection Methods ◽  
Machine Learning Methods ◽  
Brain Functional Networks ◽  
The Cost ◽  
The Brain

Network-based representations have introduced a revolution in neuroscience, expanding the understanding of the brain from the activity of individual regions to the interactions between them. This augmented network view comes at the cost of high dimensionality, which hinders both our capacity of deciphering the main mechanisms behind pathologies, and the significance of any statistical and/or machine learning task used in processing this data. A link selection method, allowing to remove irrelevant connections in a given scenario, is an obvious solution that provides improved utilization of these network representations. In this contribution we review a large set of statistical and machine learning link selection methods and evaluate them on real brain functional networks. Results indicate that most methods perform in a qualitatively similar way, with NBS (Network Based Statistics) winning in terms of quantity of retained information, AnovaNet in terms of stability and ExT (Extra Trees) in terms of lower computational cost. While machine learning methods are conceptually more complex than statistical ones, they do not yield a clear advantage. At the same time, the high heterogeneity in the set of links retained by each method suggests that they are offering complementary views to the data. The implications of these results in neuroscience tasks are finally discussed.

scholarly journals Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Ali Varzandian ◽  
Miguel Angel Sanchez Razo ◽  
Michael Richard Sanders ◽  
Akhila Atmakuru ◽  
Giuseppe Di Fatta
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Data Science ◽  
High Accuracy ◽  
Chronological Age ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Brain Data ◽  
Brain Age ◽  
The Brain

Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as “brain age”. The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain age model as biomarker is the prediction of Alzheimer's disease (AD) from structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied to this specific predictive task, some of which have achieved high accuracy at the expense of the descriptiveness of the model. This work investigates an appropriate combination of data science techniques and linear models to provide, at the same time, high accuracy and good descriptiveness. The proposed method is based on a data workflow that include typical data science methods, such as outliers detection, feature selection, linear regression, and logistic regression. In particular, a novel inductive bias is introduced in the regression model, which is aimed at improving the accuracy and the specificity of the classification task. The method is compared to other machine learning approaches for AD classification based on morphological brain data with and without the use of the brain age, including Support Vector Machines and Deep Neural Networks. This study adopts brain MRI scans of 1, 901 subjects which have been acquired from three repositories (ADNI, AIBL, and IXI). A predictive model based only on the proposed apparent brain age and the chronological age has an accuracy of 88% and 92%, respectively, for male and female subjects, in a repeated cross-validation analysis, thus achieving a comparable or superior performance than state of the art machine learning methods. The advantage of the proposed method is that it maintains the morphological semantics of the input space throughout the regression and classification tasks. The accurate predictive model is also highly descriptive and can be used to generate potentially useful insights on the predictions.

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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