Log in

Relevant bibliographies by topics / Brain-inspired automation

Contents

Journal articles
Book chapters
Conference papers

Academic literature on the topic 'Brain-inspired automation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Brain-inspired automation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Brain-inspired automation"

1

Rosemarie, Velik. "BRAIN Journal - Brain-Like Artificial Intelligence for Automation – Foundations, Concepts and Implementation Examples." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 4, no. 1-4 (2013): 26–54. https://doi.org/10.5281/zenodo.1043417.

Full text

Abstract:

ABSTRACT Over the last decades, automation technology has made serious progress and can today automate a wide range of tasks having before needed human physical and mental abilities. Nevertheless, a number of important problem domains remain that cannot yet be handled by our current machines and computers. A few prominent examples are applications involving “realworld” perception, situation assessment, and decision-making tasks. Recently, researchers have suggested to use concepts of “Brain-Like Artificial Intelligence”, i.e. concepts inspired by the functioning principles of the human or anim

APA, Harvard, Vancouver, ISO, and other styles

2

Shakya, Subarna. "Probabilistic Neural Network based Managing Algorithm for Building Automation System." December 2021 3, no. 4 (2021): 272–83. http://dx.doi.org/10.36548/jaicn.2021.4.001.

Full text

Abstract:

A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural netw

APA, Harvard, Vancouver, ISO, and other styles

3

Alparslan, Onur, Shin’ichi Arakawa, and Masayuki Murata. "SDN-Based Control of IoT Network by Brain-Inspired Bayesian Attractor Model and Network Slicing." Applied Sciences 10, no. 17 (2020): 5773. http://dx.doi.org/10.3390/app10175773.

Full text

Abstract:

One of the models in the literature for modeling the behavior of the brain is the Bayesian attractor model, which is a kind of machine-learning algorithm. According to this model, the brain assigns stochastic variables to possible decisions (attractors) and chooses one of them when enough evidence is collected from sensory systems to achieve a confidence level high enough to make a decision. In this paper, we introduce a software defined networking (SDN) application based on a brain-inspired Bayesian attractor model for identification of the current traffic pattern for the supervision and auto

APA, Harvard, Vancouver, ISO, and other styles

4

Qiu, Jane. "Research and development of artificial intelligence in China." National Science Review 3, no. 4 (2016): 538–41. http://dx.doi.org/10.1093/nsr/nww076.

Full text

Abstract:

Abstract This year saw several milestones in the development of artificial intelligence. In March, AlphaGo, a computer algorithm developed by Google's London-based company, DeepMind, beat the world champion Lee Sedol at Go, an ancient Chinese board game. In October, the same company unveiled in the journal Nature its latest technique that allows a machine to solve tasks that require logic and reasoning, such as finding its way around the London Underground using a map it has never seen before. Such progress in recent years has provided significant impetus to developing cutting-edge learning ma

APA, Harvard, Vancouver, ISO, and other styles

5

Rosemarie, Velik. "BRAIN Journal - Quo vadis, Intelligent Machine?" BRAIN - Broad Research in Artificial Intelligence and Neuroscience 1, no. 4 (2010): 13–22. https://doi.org/10.5281/zenodo.1037355.

Full text

Abstract:

ABSTRACT Artificial Intelligence (AI) is a branch of computer science concerned with making computers behave like humans. At least this was the original idea. However, it turned out that this was no easy task to solve. This article aims to give a comprehensible review on the last 60 years of artificial intelligence taking a philosophical viewpoint. It is outlined what happened so far in AI, what is currently going on in this research area, and what can be expected in the future. The goal is to mediate an understanding for the developments and changes in thinking in the course of time about how

APA, Harvard, Vancouver, ISO, and other styles

6

Arshad, Jehangir, Adan Qaisar, Atta-Ur Rehman, et al. "Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence." Applied Sciences 12, no. 21 (2022): 10813. http://dx.doi.org/10.3390/app122110813.

Full text

Abstract:

The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlik

APA, Harvard, Vancouver, ISO, and other styles

7

Mirzayev, Shoxrux. "THE ROLE OF NEURAL NETWORKS IN ECONOMETRIC MODELING AND FINANCIAL DECISION-MAKING." Iqtisodiy taraqqiyot va tahlil 2, no. 11 (2024): 185–90. https://doi.org/10.60078/2992-877x-2024-vol2-iss11-pp185-190.

Full text

Abstract:

This article examines the transformative role of neural networks in econometrics and financial decision-making, emphasizing their influence on personal finance, automation, healthcare, transportation, and human-computer interaction. Neural networks, inspired by the structure of the human brain, have the potential to revolutionize these sectors by enhancing efficiency, accuracy, and decision-making capabilities. In personal finance, they can optimize budgeting, savings, and expenditure management through automated models such as the McCulloch-Pitts neuron. In healthcare, neural networks improve

APA, Harvard, Vancouver, ISO, and other styles

8

Jia, Hepeng. "Yi Zeng: promoting good governance of artificial intelligence." National Science Review 7, no. 12 (2020): 1954–56. http://dx.doi.org/10.1093/nsr/nwaa255.

Full text

Abstract:

Abstract Artificial intelligence (AI) has developed quickly in recent years, with applications expanding from automatic driving and smart manufacturing to personal healthcare and algorithm-based social media utilization. During the COVID-19 pandemic, AI has played an essential role in identifying suspected infections, ensuring epidemic surveillance and quickening drug screening. However, many questions accompanied AI’s development. How to protect citizens’ privacy and national information security? What measures can help AI learn and practice good human behaviors and avoid unethical use of AI

APA, Harvard, Vancouver, ISO, and other styles

9

ROHAN SAINI and CHANDER DIWAKER. "Role of deep learning in business enterprises." International Journal of Science and Research Archive 14, no. 3 (2025): 1045–54. https://doi.org/10.30574/ijsra.2025.14.3.0718.

Full text

Abstract:

Deep learning technology which belongs to the machine learning domain has transformed corporate operations through both enhanced predictive capability and self-automation. Models developed from deep learning techniques process extensive information collections to reveal intricate patterns which provide organizations with useful knowledge through their operation as neural networks inspired from human brain functions. Different deep learning algorithms such as CNNs, RNNs and GANs together with autoencoders get analysed in this work while revealing their usefulness in healthcare along with retail

APA, Harvard, Vancouver, ISO, and other styles

10

Sandhyakumari Golla and Maloji Suman. "Automated Seizure Detection in Neonatal EEG using Signal Processing Algorithms." Journal of Advanced Research in Applied Sciences and Engineering Technology 31, no. 3 (2023): 220–27. http://dx.doi.org/10.37934/araset.31.3.220227.

Full text

Abstract:

The crucial challenge of identifying seizures in newborn electroencephalogram (EEG) data is the subject of this research. (ElectroEncephaloGram) EEG analysis has traditionally been labor-intensive and subjective due to the heavy reliance on human inspection by skilled electrophysiologists and neurologists. The authors suggest a novel online automatic detection approach to get over these restrictions. This approach intends to open the door for effective and reliable early seizure detection, delivering a potential development in neonatal care and neurological research. It does this by utilizing

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Brain-inspired automation"

1

Cheng, Jingde. "Can “Neuromorphic Completeness” and “Brain-Inspired Computing” Provide a Promising Platform for Artificial General Intelligence?" In Advances in Intelligent Automation and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_14.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Xie, Pengyi, Jiangbin Zheng, Qianru Wei, and Yuke Wang. "Automatic Threshold Selection Method for SAR Edge Detection." In Advances in Brain Inspired Cognitive Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39431-8_51.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Krishnan, Reshmy, Amir Hussain, and Sherimon P.C. "Conceptual Clustering of Documents for Automatic Ontology Generation." In Advances in Brain Inspired Cognitive Systems. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38786-9_27.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Hong, Bei, Jing Liu, Weifu Li, Chi Xiao, Qiwei Xie, and Hua Han. "Fully Automatic Synaptic Cleft Detection and Segmentation from EM Images Based on Deep Learning." In Advances in Brain Inspired Cognitive Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00563-4_7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Peiris, Himashi, and Zhaolin Chen. "Bilateral Hippocampi Segmentation in Low Field MRIs Using Mutual Feature Learning via Dual-Views." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83008-2_2.

Full text

Abstract:

Abstract Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective, which eliminates the need for sedation in children, though they often suffer from lower image quality. In this paper, we present a novel deep-learning approach for the automatic segmentation of bilateral hippocampi in low-field MRIs. Extending recent advancements in infant brain segmentation to underserved communities through the use of l

APA, Harvard, Vancouver, ISO, and other styles

6

Grundy, John G., and Ashley Chung-Fat-Yim. "Chapter 12. Domain-general electrophysiological changes associated with bilingualism." In Studies in Bilingualism. John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/sibil.64.12gru.

Full text

Abstract:

Electroencephalogram (EEG) has been instrumental in research examining the effects of bilingualism on cognition, and much of this research has been inspired and spearheaded by Ellen Bialystok. In this chapter, we explore recent developments in EEG research that highlight the complexity of the bilingual experience and its impact on domain-general cognitive outcomes. First, even in the absence of behavioral differences, ERP studies consistently show that bilinguals are more efficient at processing information than monolinguals on executive function tasks. Second, resting-state EEG studies demons

APA, Harvard, Vancouver, ISO, and other styles

7

Konar, Debanjan, Siddhartha Bhattacharyya, and Bijaya Ketan Panigrahi. "QIBDS Net: A Quantum-Inspired Bi-Directional Self-supervised Neural Network Architecture for Automatic Brain MR Image Segmentation." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34872-4_10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Srivastava, Shiddarth, and Rashmi Sharma. "Data Analysis Tools for Neural Data Cognitive Computing." In Exploring Future Opportunities of Brain-Inspired Artificial Intelligence. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6980-4.ch009.

Full text

Abstract:

The ability to draw conclusions and take action from data hasn't altered all that much despite significant technological developments in recent years. Applications are still typically created to carry out predefined tasks or automate business procedures; therefore, the logic must be coded to account for all possible usage scenarios. They do not grow from their mistakes or adjust to changes in the data. Although they are cheaper and faster, computers aren't substantially smarter. Of course, people now aren't all that much brighter than they were in the past. For both humans and machines, that i

APA, Harvard, Vancouver, ISO, and other styles

9

Pérez, Rafael Pérez y., and Mike Sharples. "Inspired by the brain." In An Introduction to Narrative Generators. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198876601.003.0010.

Full text

Abstract:

Abstract In the 1980s and 1890s, a typical neural network comprised hundreds of elements organized in layers. Nowadays, deep neural networks (DNN) interconnect millions of those artificial neurons. This chapter introduces DNNs. Its objective is to discuss the basic concepts behind DNNs, such as parallel distributed system, training, learning, and transformers, as well as to illustrate how these concepts are used to generate texts. We describe how to design a narrative generator employing this approach. The chapter ends with a summary of some automatic storytellers based on DNNs like GPT-3, som

APA, Harvard, Vancouver, ISO, and other styles

10

Tivive, Fok Hing Chi, and Abdesselam Bouzerdoum. "A Brain-Inspired Visual Pattern Recognition Architecture and Its Applications." In Pattern Recognition Technologies and Applications. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch011.

Full text

Abstract:

With the ever-increasing utilization of imagery in scientific, industrial, civilian, and military applications, visual pattern recognition has been thriving as a research field and has become an essential enabling technology for many applications. In this chapter, we present a brain-inspired pattern recognition architecture that can easily be adapted to solve various real-world visual pattern recognition tasks. The architecture has the ability to extract visual features from images and classify them within the same network structure; in other words, it integrates the feature extraction stage w

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Brain-inspired automation"

1

Genssler, Paul R., Lilas Alrahis, Ozgur Sinanoglu, and Hussam Amrouch. "HDCircuit: Brain-Inspired HyperDimensional Computing for Circuit Recognition." In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546587.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Zhao, Hangpiao, and Fengzhen Tang. "Semantic Map Construction Method Based on Brain-Inspired SLAM." In 2024 IEEE 14th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2024. http://dx.doi.org/10.1109/cyber63482.2024.10749279.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ao, Tianyong, Xianze Cao, Yaolin Tang, Le Fu, and Yi Zhou. "A Brain-Inspired Approach for Compliant Force Interaction Control under Predefined Tasks." In 2024 7th International Conference on Robotics, Control and Automation Engineering (RCAE). IEEE, 2024. https://doi.org/10.1109/rcae62637.2024.10834211.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Kwon, Hyukjun, Kangwon Kim, Junyoung Lee, et al. "Brain-Inspired Hyperdimensional Computing in the Wild: Lightweight Symbolic Learning for Sensorimotor Controls of Wheeled Robots." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610176.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Roy, Kaushik, Mrigank Sharad, Deliang Fan, and Karthik Yogendra. "Brain-inspired computing with spin torque devices." In Design Automation and Test in Europe. IEEE Conference Publications, 2014. http://dx.doi.org/10.7873/date.2014.245.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Roy, Kaushik, Mrigank Sharad, Deliang Fan, and Karthik Yogendra. "Brain-inspired computing with spin torque devices." In Design Automation and Test in Europe. IEEE Conference Publications, 2014. http://dx.doi.org/10.7873/date2014.245.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Clermidy, Fabien, Rodolphe Heliot, Alexandre Valentian, et al. "Advanced technologies for brain-inspired computing." In 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 2014. http://dx.doi.org/10.1109/aspdac.2014.6742951.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Milford, Michael J., and Adam Jacobson. "Brain-inspired sensor fusion for navigating robots." In 2013 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2013. http://dx.doi.org/10.1109/icra.2013.6630980.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Bai, Kangjun, Jialing Li, Kian Hamedani, and Yang Yi. "Enabling a new era of brain-inspired computing." In DAC '18: The 55th Annual Design Automation Conference 2018. ACM, 2018. http://dx.doi.org/10.1145/3195970.3196044.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Lepora, Nathan F., J. Charlie Sullivan, Ben Mitchinson, Martin Pearson, Kevin Gurney, and Tony J. Prescott. "Brain-inspired Bayesian perception for biomimetic robot touch." In 2012 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2012. http://dx.doi.org/10.1109/icra.2012.6224815.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!