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Journal articles on the topic 'Brain-inspired automation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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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.

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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

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Khan, Fatima Hameed, Muhammad Adeel Pasha, and Shahid Masud. "Advancements in Microprocessor Architecture for Ubiquitous AI—An Overview on History, Evolution, and Upcoming Challenges in AI Implementation." Micromachines 12, no. 6 (2021): 665. http://dx.doi.org/10.3390/mi12060665.

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Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resul

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Zhao, Ziyan, Pengkai Xiao, Jiacun Wang, et al. "Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time." Mathematics 12, no. 1 (2023): 9. http://dx.doi.org/10.3390/math12010009.

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Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing p

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Šíma, Jiří. "Energy Complexity of Recurrent Neural Networks." Neural Computation 26, no. 5 (2014): 953–73. http://dx.doi.org/10.1162/neco_a_00579.

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Recently a new so-called energy complexity measure has been introduced and studied for feedforward perceptron networks. This measure is inspired by the fact that biological neurons require more energy to transmit a spike than not to fire, and the activity of neurons in the brain is quite sparse, with only about 1% of neurons firing. In this letter, we investigate the energy complexity of recurrent networks, which counts the number of active neurons at any time instant of a computation. We prove that any deterministic finite automaton with m states can be simulated by a neural network of optima

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Ferreira, Lucas Costa Lima, André Luiz Caliari Costa, and Angela Leite Moreno. "Classification of brain tumors in magnetic resonance images using a Convolutional Neural Network." Journal of Engineering and Exact Sciences 11, no. 1 (2025): 21317. https://doi.org/10.18540/jcecvl11iss1pp21317.

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The National Cancer Institute estimates that 1.5%–1.8% of malignant tumors occur in the central nervous system (CNS), with 88% affecting the brain. In 2020, this impacted approximately 11,000 Brazilians. Brain tumors are classified as primary, originating in the brain or nearby tissues, or secondary, spreading from other organs. Diagnosis of these tumors relies on imaging techniques such as MRI and CT, with MRI preferred for its non-ionizing radiation and superior spatial resolution. Recent advances in machine learning and deep learning, particularly convolutional neural networks (CNNs), have

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15

Fountain, Andrew, and Cory Merkel. "Effect of biologically-motivated energy constraints on liquid state machine dynamics and classification performance." Neuromorphic Computing and Engineering 2, no. 2 (2022): 024005. http://dx.doi.org/10.1088/2634-4386/ac5d1f.

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Abstract Equipping edge devices with intelligent behavior opens up new possibilities for automating the decision making in extreme size, weight, and power-constrained application domains. To this end, several recent lines of research are aimed at the design of artificial intelligence hardware accelerators that have significantly reduced footprint and power demands compared to conventional CPU/GPU systems. However, despite some key advancements, the majority of work in this area assumes that there is an unlimited supply of energy available for computation, which is not realistic in the case of

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-, Swapnil Bilgoji, and Shubha B. Baravani -. "Cognitive Robotics: Where Brain Science Meets Automation." International Journal on Science and Technology 16, no. 2 (2025). https://doi.org/10.71097/ijsat.v16.i2.6319.

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Cognitive robotics is an interdisciplinary field that fuses artificial intelligence, neuroscience, robotics, and cognitive science to create intelligent robots capable of adaptive, goal-driven behavior. This paper presents a comprehensive analysis of cognitive robotics systems, emphasizing biologically inspired models, sensory integration, and learning mechanisms. By integrating brain-like processes, cognitive robots can learn from interaction, adapt to complex environments, and perform tasks traditionally reserved for humans. We present experimental results from a robotic testbed implementing

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17

Wang, Wei, Zhaowei Shang, and Chengxing Li. "Brain-inspired semantic data augmentation for multi-style images." Frontiers in Neurorobotics 18 (March 26, 2024). http://dx.doi.org/10.3389/fnbot.2024.1382406.

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Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new br

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18

Rosemarie, Velik, and Bruckner Dietmar. "A Bionic Approach to Dynamic, Multimodal Scene Perception and Interpretation in Buildings." April 22, 2009. https://doi.org/10.5281/zenodo.1080973.

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Today, building automation is advancing from simple monitoring and control tasks of lightning and heating towards more and more complex applications that require a dynamic perception and interpretation of different scenes occurring in a building. Current approaches cannot handle these newly upcoming demands. In this article, a bionically inspired approach for multimodal, dynamic scene perception and interpretation is presented, which is based on neuroscientific and neuro-psychological research findings about the perceptual system of the human brain. This approach bases on data from diverse sen

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19

MaBouDi, HaDi, Mark Roper, Marie-Geneviève Guiraud, Mikko Juusola, Lars Chittka, and James AR Marshall. "A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees." eLife 14 (July 1, 2025). https://doi.org/10.7554/elife.89929.

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Bees’ remarkable visual learning abilities make them ideal for studying active information acquisition and representation. Here, we develop a biologically inspired model to examine how flight behaviours during visual scanning shape neural representation in the insect brain, exploring the interplay between scanning behaviour, neural connectivity, and visual encoding efficiency. Incorporating non-associative learning—adaptive changes without reinforcement—and exposing the model to sequential natural images during scanning, we obtain results that closely match neurobiological observations. Active

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20

Okonkwo, Joshua Ifeanyi, Mohamed S. Abdelfattah, Peyman Mirtaheri, and Ali Muhtaroglu. "Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge applications." Frontiers in Neuroscience 18 (September 23, 2024). http://dx.doi.org/10.3389/fnins.2024.1431222.

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Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity o

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Faust, Kevin, Michael K. Lee, Anglin Dent, et al. "Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning." Neuro-Oncology Advances 4, no. 1 (2022). http://dx.doi.org/10.1093/noajnl/vdac001.

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Abstract Background Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation. Methods To address

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da Silva, Leonardo. "LEVERAGING ARTIFICIAL NEURAL NETWORKS FOR OPTIMIZING ENERGY CONSUMPTION IN COMMERCIAL BUILDINGS." Revista ft, January 1, 2018, 45–46. http://dx.doi.org/10.69849/revistaft/ar10201801011045.

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Optimizing energy consumption in commercial buildings is becoming increasingly essential due to escalating energy costs and the demand for sustainable practices. Artificial Neural Networks (ANNs) have emerged as a powerful tool to address these challenges by offering real-time, intelligent solutions that adapt to usage patterns and environmental conditions. Inspired by the human brain, ANNs consist of layers of artificial neurons connected through weighted links. They are trained using historical and real-time data to recognize complex consumption patterns, predict future energy needs, and eff

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