Unraveling Somatotopic Organization in the Human Brain using Machine Learning and Adaptive Supervoxel-based Parcellations

Brain wave signal is a bioelectric phenomenon reflecting activities in human brain. In this paper, we firstly introduce brain wave-based identity recognition techniques and the state-of-the-art work. We then analyze important features of brain wave and present challenges confronted by its applications. Further, we evaluate the security and practicality of using brain wave in identity recognition and anticounterfeiting authentication and describe use cases of several machine learning methods in brain wave signal processing. Afterwards, we survey the critical issues of characteristic extraction, classification, and selection involved in brain wave signal processing. Finally, we propose several brain wave-based identity recognition techniques for further studies and conclude this paper.

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AUTOMATIC COLORIZATION USING CONVOLUTIONAL NEURAL NETWORKS

International Journal of Computer Science and Mobile Computing ◽

10.47760/ijcsmc.2021.v10i07.002 ◽

2021 ◽

Vol 10 (7) ◽

pp. 10-19

Author(s):

N. Lakshmi Prasanna ◽

Sk. Sohal Rehman ◽

V. Naga Phani ◽

S. Koteswara Rao ◽

T. Ram Santosh

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

User Interface ◽

Human Brain ◽

Learning Techniques ◽

Gray Scale Image ◽

User Intervention ◽

Better Than ◽

Grayscale Images

Automatic Colorization helps to hallucinate what an input gray scale image would look like when colorized. Automatic coloring makes it look and feel better than Grayscale. One of the most important technologies used in Machine learning is Deep Learning. Deep learning is nothing but to train the computer with certain algorithms which imitates the working of the human brain. Some of the areas in which it is used are medical, Industrial Automation, Electronics etc. The main objective of this project is coloring Grayscale images. We have umbrellaed the concepts of convolutional neural networks along with the use of the Opencv library in Python to construct our desired model. A user interface has also been fabricated to get personalized inputs using PIL. The user had to give details about boundaries, what colors to put, etc. Colorization requires considerable user intervention and remains a tedious, time consuming, and expensive task. So, in this paper we try to build a model to colorize the grayscale images automatically by using some modern deep learning techniques. In colorization task, the model needs to find characteristics to map grayscale images with colored ones.

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Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia

10.1101/660779 ◽

2019 ◽

Cited By ~ 2

Author(s):

Xiaowei Zhu ◽

Bo Zhou ◽

Reenal Pattni ◽

Kelly Gleason ◽

Chunfeng Tan ◽

...

Keyword(s):

Machine Learning ◽

Human Brain ◽

Whole Genome Amplification ◽

Mobile Element ◽

Anatomical Location ◽

Whole Genome ◽

Genetic Associations ◽

Genome Amplification ◽

Human Neurons ◽

Neuropsychiatric Diseases

AbstractActive retrotransposons in the human genome (L1, Alu and SVA elements) can create genomic mobile element insertions (MEIs) in both germline and somatic tissue1. Specific somatic MEIs have been detected at high levels in human cancers2, and at lower to medium levels in human brains3. Dysregulation of somatic retrotransposition in the human brain has been hypothesized to contribute to neuropsychiatric diseases4, 5. However, individual somatic MEIs are present in small proportions of cells at a given anatomical location, and thus standard whole-genome sequencing (WGS) presents a difficult signal-to-noise problem, while single-cell approaches suffer from limited scalability and experimental artifacts introduced by enzymatic whole-genome amplification6. Previous studies produced widely differing estimates for the somatic retrotransposition rates in human brain3, 6–8. Here, we present a highly precise machine learning method (RetroSom) to directly identify somatic L1 and Alu insertions in <1% cells from 200× deep WGS, which allows circumventing the restrictions of whole-genome amplification. Using RetroSom we confirmed a lower rate of retrotransposition for individual somatic L1 insertions in human neurons. We discovered that anatomical distribution of somatic L1 insertion is as widespread in glia as in neurons, and across both hemispheres of the brain, indicating retrotransposition occurs during early embryogenesis. We characterized two of the detected brain-specific L1 insertions in great detail in neurons and glia from a donor with schizophrenia. Both insertions are within introns of genes active in brain (CNNM2, FRMD4A) in regions with multiple genetic associations with neuropsychiatric disorders9–11. Gene expression was significantly reduced by both somatic insertions in a reporter assay. Our results provide novel insights into the potential for pathological effects of somatic retrotransposition in the human brain, now including the large glial fraction. RetroSom has broad applicability in all disease states where somatic retrotransposition is expected to play a role, such as autoimmune disorders and cancer.

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Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning?

American Journal of Neuroradiology ◽

10.3174/ajnr.a6040 ◽

2019 ◽

Vol 40 (5) ◽

pp. E24-E25

Author(s):

A. Dasgupta ◽

T. Gupta

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Human Brain

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The Human Intelligence vs. Artificial Intelligence: Issues and Challenges in Computer Assisted Language Learning

International Journal of English Linguistics ◽

10.5539/ijel.v8n5p259 ◽

2018 ◽

Vol 8 (5) ◽

pp. 259

Author(s):

Mohammed Ali

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Human Brain ◽

Language Learning ◽

Language Processing ◽

Human Intelligence ◽

Computer Assisted ◽

Learning Programs ◽

Face Detection And Recognition ◽

Learning Software

In this study, the researcher has advocated the importance of human intelligence in language learning since software or any Learning Management System (LMS) cannot be programmed to understand the human context as well as all the linguistic structures contextually. This study examined the extent to which language learning is perilous to machine learning and its programs such as Artificial Intelligence (AI), Pattern Recognition, and Image Analysis used in much assistive learning techniques such as voice detection, face detection and recognition, personalized assistants, besides language learning programs. The researchers argue that language learning is closely associated with human intelligence, human neural networks and no computers or software can claim to replace or replicate those functions of human brain. This study thus posed a challenge to natural language processing (NLP) techniques that claimed having taught a computer how to understand the way humans learn, to understand text without any clue or calculation, to realize the ambiguity in human languages in terms of the juxtaposition between the context and the meaning, and also to automate the language learning process between computers and humans. The study cites evidence of deficiencies in such machine learning software and gadgets to prove that in spite of all technological advancements there remain areas of human brain and human intelligence where a computer or its software cannot enter. These deficiencies highlight the limitations of AI and super intelligence systems of machines to prove that human intelligence would always remain superior.

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