Articles de revues : « Frontière neurale »

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Xu Mingliang, 徐明亮, 李芳媛 Li Fangyuan, 刘岳圻 Liu Yueqi, 张瑾慧 Zhang Jinhui, 师亚洲 Shi Yazhou et 何飞 He Fei. « 植入式多模态神经接口前沿进展 ». Chinese Journal of Lasers 50, n^o 15 (2023) : 1507301. http://dx.doi.org/10.3788/cjl221482.

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Rey, Federica, Bianca Barzaghini, Alessandra Nardini, Matteo Bordoni, Gian Vincenzo Zuccotti, Cristina Cereda, Manuela Teresa Raimondi et Stephana Carelli. « Advances in Tissue Engineering and Innovative Fabrication Techniques for 3-D-Structures : Translational Applications in Neurodegenerative Diseases ». Cells 9, n^o 7 (7 juillet 2020) : 1636. http://dx.doi.org/10.3390/cells9071636.

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In the field of regenerative medicine applied to neurodegenerative diseases, one of the most important challenges is the obtainment of innovative scaffolds aimed at improving the development of new frontiers in stem-cell therapy. In recent years, additive manufacturing techniques have gained more and more relevance proving the great potential of the fabrication of precision 3-D scaffolds. In this review, recent advances in additive manufacturing techniques are presented and discussed, with an overview on stimulus-triggered approaches, such as 3-D Printing and laser-based techniques, and deposition-based approaches. Innovative 3-D bioprinting techniques, which allow the production of cell/molecule-laden scaffolds, are becoming a promising frontier in disease modelling and therapy. In this context, the specific biomaterial, stiffness, precise geometrical patterns, and structural properties are to be considered of great relevance for their subsequent translational applications. Moreover, this work reports numerous recent advances in neural diseases modelling and specifically focuses on pre-clinical and clinical translation for scaffolding technology in multiple neurodegenerative diseases.

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Liu, Feng, Jianjun Pang et Zhiwei Xu. « Multi-Objective Optimization of Injection Molding Process Parameters for Moderately Thick Plane Lens Based on PSO-BPNN, OMOPSO, and TOPSIS ». Processes 12, n^o 1 (22 décembre 2023) : 36. http://dx.doi.org/10.3390/pr12010036.

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Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated.

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Justen, Lennart, Duncan Carlsmith, Susan M. Paskewitz, Lyric C. Bartholomay et Gebbiena M. Bron. « Identification of public submitted tick images : A neural network approach ». PLOS ONE 16, n^o 12 (2 décembre 2021) : e0260622. http://dx.doi.org/10.1371/journal.pone.0260622.

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Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call “TickIDNet,” that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network’s ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet’s performance can be increased by using confidence thresholds to introduce an “unsure” class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.

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Le Merrer, Erwan, Patrick Pérez et Gilles Trédan. « Adversarial frontier stitching for remote neural network watermarking ». Neural Computing and Applications 32, n^o 13 (17 août 2019) : 9233–44. http://dx.doi.org/10.1007/s00521-019-04434-z.

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Tsionas, Mike, Christopher F. Parmeter et Valentin Zelenyuk. « Bayesian Artificial Neural Networks for frontier efficiency analysis ». Journal of Econometrics 236, n^o 2 (octobre 2023) : 105491. http://dx.doi.org/10.1016/j.jeconom.2023.105491.

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Jin, Ding. « Portfolio Management Based on TMT Sector : Comparative Study between Basic Qualitative and Model-Based Approach ». BCP Business & ; Management 38 (2 mars 2023) : 579–91. http://dx.doi.org/10.54691/bcpbm.v38i.3742.

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Nowadays, more and more investment techniques are incorporating model-based techniques to facilitate portfolio management process; however, techniques that could predict future stock expected returns are relatively scarce. This study mainly focuses on using model-based methods to evaluate stocks in the TMT (Technology, Media, Telecom) sector based on historical data for the last four years. LSTM (Long Short-Term Memory) neural network and Fama-French three factor models are employed in this study to predict the future expected return of the stocks and further, evaluate whether the stock could come into the optimal portfolio. Efficient frontiers are drawn using the variance of expected return against the mean of future expected return. Then investment utility function is set up along with the efficient frontier to make optimizer to get the best weight of the optimal portfolio. To justify if the model-based approach is robust, comparative study is done between basic qualitative approach and model-based approach. Two parallel approaches will use different methods as well as metrics to evaluate the same set of competitor stocks and generate the optimal portfolio. The results have shown that both approaches have the same optimal portfolio and the model-based approach is justified. Thus, this quantitative model-based approach is robust and applicable for investment since it could generate consistent result as the basic qualitative approach and it is more explicit in data.

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Akter, Mst Shapna, Hossain Shahriar, Reaz Chowdhury et M. R. C. Mahdy. « Forecasting the Risk Factor of Frontier Markets : A Novel Stacking Ensemble of Neural Network Approach ». Future Internet 14, n^o 9 (25 août 2022) : 252. http://dx.doi.org/10.3390/fi14090252.

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Forecasting the risk factor of the financial frontier markets has always been a very challenging task. Unlike an emerging market, a frontier market has a missing parameter named “volatility”, which indicates the market’s risk and as a result of the absence of this missing parameter and the lack of proper prediction, it has almost become difficult for direct customers to invest money in frontier markets. However, the noises, seasonality, random spikes and trends of the time-series datasets make it even more complicated to predict stock prices with high accuracy. In this work, we have developed a novel stacking ensemble of the neural network model that performs best on multiple data patterns. We have compared our model’s performance with the performance results obtained by using some traditional machine learning ensemble models such as Random Forest, AdaBoost, Gradient Boosting Machine and Stacking Ensemble, along with some traditional deep learning models such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term (BiLSTM). We have calculated the missing parameter named “volatility” using stock price (Close price) for 20 different companies of the frontier market and then made predictions using the aforementioned machine learning ensemble models, deep learning models and our proposed stacking ensemble of the neural network model. The statistical evaluation metrics RMSE and MAE have been used to evaluate the performance of the models. It has been found that our proposed stacking ensemble neural network model outperforms all other traditional machine learning and deep learning models which have been used for comparison in this paper. The lowest RMSE and MAE values we have received using our proposed model are 0.3626 and 0.3682 percent, respectively, and the highest RMSE and MAE values are 2.5696 and 2.444 percent, respectively. The traditional ensemble learning models give the highest RMSE and MAE error rate of 20.4852 and 20.4260 percent, while the deep learning models give 15.2332 and 15.1668 percent, respectively, which clearly states that our proposed model provides a very low error value compared with the traditional models.

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Furber, Steve, et Steve Temple. « Neural systems engineering ». Journal of The Royal Society Interface 4, n^o 13 (28 novembre 2006) : 193–206. http://dx.doi.org/10.1098/rsif.2006.0177.

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The quest to build an electronic computer based on the operational principles of biological brains has attracted attention over many years. The hope is that, by emulating the brain, it will be possible to capture some of its capabilities and thereby bridge the very large gulf that separates mankind from machines. At present, however, knowledge about the operational principles of the brain is far from complete, so attempts at emulation must employ a great deal of assumption and guesswork to fill the gaps in the experimental evidence. The sheer scale and complexity of the human brain still defies attempts to model it in its entirety at the neuronal level, but Moore's Law is closing this gap and machines with the potential to emulate the brain (so far as we can estimate the computing power required) are no more than a decade or so away. Do computer engineers have something to contribute, alongside neuroscientists, psychologists, mathematicians and others, to the understanding of brain and mind, which remains as one of the great frontiers of science?

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Cohen, Avis H. « Neural Regeneration and Transplantation (Frontiers of Clinical Neuroscience Vol. 6) ». Trends in Neurosciences 13, n^o 2 (février 1990) : 77–78. http://dx.doi.org/10.1016/0166-2236(90)90074-k.

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van Drongelen, Wim. « Modeling Neural Activity ». ISRN Biomathematics 2013 (7 mars 2013) : 1–37. http://dx.doi.org/10.1155/2013/871472.

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This paper provides an overview of different types of models for studying activity of nerve cells and their networks with a special emphasis on neural oscillations. One part describes the neuronal models based on the Hodgkin and Huxley formalism first described in the 1950s. It is discussed how further simplifications of this formalism enable mathematical analysis of the process of neural excitability. The focus of the paper’s second component is on network activity. Understanding network function is one of the important frontiers remaining in neuroscience. At present, experimental techniques can only provide global recordings or samples of the activity of the huge networks that form the nervous system. Models in neuroscience can therefore play a critical role by providing a framework for integration of necessarily incomplete datasets, thereby providing insight into the mechanisms of neural function. Network models can either explicitly contain individual network nodes that model the neurons, or they can be based on representations of compound population activity. The latter approach was pioneered by Wilson and Cowan in the 1970s. Finally I provide an overview and discuss how network models are employed in the study of neuronal network pathology such as epilepsy.

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Rao, Tejus, Prashanth Alle et Sumit Thakar. « “Frontier Wire Probing” Technique for Transvenous Embolization of Carotid Cavernous Fistulae Using Topographical Landmarks ». Neurology India 72, n^o 1 (2024) : 24–27. http://dx.doi.org/10.4103/neurol-india.neurol-india-d-23-00511.

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Radovanovic, Sandro, Milan Radojicic et Gordana Savic. « Two-phased DEA-MLA approach for predicting efficiency of NBA players ». Yugoslav Journal of Operations Research 24, n^o 3 (2014) : 347–58. http://dx.doi.org/10.2298/yjor140430030r.

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In sports, a calculation of efficiency is considered to be one of the most challenging tasks. In this paper, DEA is used to evaluate an efficiency of the NBA players, based on multiple inputs and multiple outputs. The efficiency is evaluated for 26 NBA players at the guard position based on existing data. However, if we want to generate the efficiency for a new player, we would have to re-conduct the DEA analysis. Therefore, to predict the efficiency of a new player, machine learning algorithms are applied. The DEA results are incorporated as an input for the learning algorithms, defining thereby an efficiency frontier function form with high reliability. In this paper, linear regression, neural network, and support vector machines are used to predict an efficiency frontier. The results have shown that neural networks can predict the efficiency with an error less than 1%, and the linear regression with an error less than 2%.

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Sanei, Reza, Farhad Hosseinzadeh lotfi, Mohammad Fallah et Farzad Movahedi Sobhani. « An Estimation of an Acceptable Efficiency Frontier Having an Optimum Resource Management Approach, with a Combination of the DEA-ANN-GA Technique (A Case Study of Branches of an Insurance Company) ». Mathematics 10, n^o 23 (29 novembre 2022) : 4503. http://dx.doi.org/10.3390/math10234503.

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In this paper, a novel artificial intelligence technique for the estimation of near-optimal resource management is proposed. The model utilizes a two-stage data envelopment analysis to find the best-practice frontier of the decision-making units. By employing this data, a supervised multi-layer Artificial Neural Network is exercised. This network is capable of predicting the frontier for the near future by receiving input and mediator variables. In the next step, a genetic algorithm is formed to find an optimal input value for the artificial neural network, such that the overall performance of decision-making units in the near future is maximized. The proposed algorithm allows the managers to set some restrictions on the whole system, including the minimum efficiency and the maximum change on resources. The performance of the presented technique is reviewed on 31 branches of an insurance company, during the years 2015 to 2018. The results show that the developed algorithm can efficiently maximize the overall performance of decision-making units.

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Wang, Zichi, Guorui Feng, Hanzhou Wu et Xinpeng Zhang. « Data Hiding in Neural Networks for Multiple Receivers [Research Frontier] ». IEEE Computational Intelligence Magazine 16, n^o 4 (novembre 2021) : 70–84. http://dx.doi.org/10.1109/mci.2021.3108305.

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Modha, Dharmendra S., Filipp Akopyan, Alexander Andreopoulos, Rathinakumar Appuswamy, John V. Arthur, Andrew S. Cassidy, Pallab Datta et al. « Neural inference at the frontier of energy, space, and time ». Science 382, n^o 6668 (20 octobre 2023) : 329–35. http://dx.doi.org/10.1126/science.adh1174.

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Computing, since its inception, has been processor-centric, with memory separated from compute. Inspired by the organic brain and optimized for inorganic silicon, NorthPole is a neural inference architecture that blurs this boundary by eliminating off-chip memory, intertwining compute with memory on-chip, and appearing externally as an active memory chip. NorthPole is a low-precision, massively parallel, densely interconnected, energy-efficient, and spatial computing architecture with a co-optimized, high-utilization programming model. On the ResNet50 benchmark image classification network, relative to a graphics processing unit (GPU) that uses a comparable 12-nanometer technology process, NorthPole achieves a 25 times higher energy metric of frames per second (FPS) per watt, a 5 times higher space metric of FPS per transistor, and a 22 times lower time metric of latency. Similar results are reported for the Yolo-v4 detection network. NorthPole outperforms all prevalent architectures, even those that use more-advanced technology processes.

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Chang, Alexander M., Jessica G. Freeze et Victor S. Batista. « Hammett neural networks : prediction of frontier orbital energies of tungsten–benzylidyne photoredox complexes ». Chemical Science 10, n^o 28 (2019) : 6844–54. http://dx.doi.org/10.1039/c9sc02339a.

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The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten–alkylidyne complexes of the form W(CArR)L₄X.

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Islam, Md Mahbubul, Nusrat Tasnim et Joong-Hwan Baek. « Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks ». Inventions 5, n^o 2 (13 avril 2020) : 16. http://dx.doi.org/10.3390/inventions5020016.

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Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

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Geetha, Dr V., Dr C. K. Gomathy, Mr S. R. Bathrinathan et Shiva Koushik Sripada. « AI AND IMAGINATION : BRIDGING THE GAP BETWEEN CREATIVITY AND MACHINES ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n^o 02 (29 février 2024) : 1–11. http://dx.doi.org/10.55041/ijsrem28888.

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Abstract—The convergence of artificial intelligence and human imagination is reshaping the creative landscape. This exploration examines how AI has evolved from its traditional role to become a catalyst for innovation. Through advanced generative models and neural networks, AI goes beyond automation, allowing creativity to flourish without limitations. However, ethical considerations and societal implications arise as AI-infused imagination explores new frontiers in artistry and problem-solving. The evolving partnership between human ingenuity and machine intelligence opens up collaborative opportunities that unveil exciting possibilities in creativity and innovation. Keywords—Artificial Intelligence, Human imagination, neural networks, generative model, automation.

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Dalhoum, Abdel Latif Abu, et Mohammed Al-Rawi. « High-Order Neural Networks are Equivalent to Ordinary Neural Networks ». Modern Applied Science 13, n^o 2 (27 janvier 2019) : 228. http://dx.doi.org/10.5539/mas.v13n2p228.

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Equivalence of computational systems can assist in obtaining abstract systems, and thus enable better understanding of issues related their design and performance. For more than four decades, artificial neural networks have been used in many scientific applications to solve classification problems as well as other problems. Since the time of their introduction, multilayer feedforward neural network referred as Ordinary Neural Network (ONN), that contains only summation activation (Sigma) neurons, and multilayer feedforward High-order Neural Network (HONN), that contains Sigma neurons, and product activation (Pi) neurons, have been treated in the literature as different entities. In this work, we studied whether HONNs are mathematically equivalent to ONNs. We have proved that every HONN could be converted to some equivalent ONN. In most cases, one just needs to modify the neuronal transfer function of the Pi neuron to convert it to a Sigma neuron. The theorems that we have derived clearly show that the original HONN and its corresponding equivalent ONN would give exactly the same output, which means; they can both be used to perform exactly the same functionality. We also derived equivalence theorems for several other non-standard neural networks, for example, recurrent HONNs and HONNs with translated multiplicative neurons. This work rejects the hypothesis that HONNs and ONNs are different entities, a conclusion that might initiate a new research frontier in artificial neural network research.

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Guo, Dongsheng, et Yunong Zhang. « Novel Recurrent Neural Network for Time-Varying Problems Solving [Research Frontier] ». IEEE Computational Intelligence Magazine 7, n^o 4 (novembre 2012) : 61–65. http://dx.doi.org/10.1109/mci.2012.2215139.

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Wang, Shouhong. « Adaptive non-parametric efficiency frontier analysis : a neural-network-based model ». Computers & ; Operations Research 30, n^o 2 (février 2003) : 279–95. http://dx.doi.org/10.1016/s0305-0548(01)00095-8.

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Sun, Gang, et Shuyue Wang. « A review of the artificial neural network surrogate modeling in aerodynamic design ». Proceedings of the Institution of Mechanical Engineers, Part G : Journal of Aerospace Engineering 233, n^o 16 (26 juillet 2019) : 5863–72. http://dx.doi.org/10.1177/0954410019864485.

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Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.

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Pietrini, Pietro. « Novel frontiers in brain imaging : Understanding the neural roots of human behavior ». International Journal of Psychophysiology 108 (octobre 2016) : 16–17. http://dx.doi.org/10.1016/j.ijpsycho.2016.07.057.

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Kong, Lingzhuo, Xiaonan Guo, Yuting Shen, Le Xu, Huimin Huang, Jing Lu et Shaohua Hu. « Pushing the Frontiers : Optogenetics for Illuminating the Neural Pathophysiology of Bipolar Disorder ». International Journal of Biological Sciences 19, n^o 14 (2023) : 4539–51. http://dx.doi.org/10.7150/ijbs.84923.

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Pell, Marc D., et Sonja A. Kotz. « Comment : The Next Frontier : Prosody Research Gets Interpersonal ». Emotion Review 13, n^o 1 (janvier 2021) : 51–56. http://dx.doi.org/10.1177/1754073920954288.

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Neurocognitive models (e.g., Schirmer & Kotz, 2006) have helped to characterize how listeners incrementally derive meaning from vocal expressions of emotion in spoken language, what neural mechanisms are involved at different processing stages, and their relative time course. But how can these insights be applied to communicative situations in which prosody serves a predominantly interpersonal function? This comment examines recent data highlighting the dynamic interplay of prosody and language, when vocal attributes serve the sociopragmatic goals of the speaker or reveal interpersonal information that listeners use to construct a mental representation of what is being communicated. Our comment serves as a beacon to researchers interested in how the neurocognitive system “makes sense” of socioemotive aspects of prosody.

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Kozai, Takashi. « The History and Horizons of Microscale Neural Interfaces ». Micromachines 9, n^o 9 (6 septembre 2018) : 445. http://dx.doi.org/10.3390/mi9090445.

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Microscale neural technologies interface with the nervous system to record and stimulate brain tissue with high spatial and temporal resolution. These devices are being developed to understand the mechanisms that govern brain function, plasticity and cognitive learning, treat neurological diseases, or monitor and restore functions over the lifetime of the patient. Despite decades of use in basic research over days to months, and the growing prevalence of neuromodulation therapies, in many cases the lack of knowledge regarding the fundamental mechanisms driving activation has dramatically limited our ability to interpret data or fine-tune design parameters to improve long-term performance. While advances in materials, microfabrication techniques, packaging, and understanding of the nervous system has enabled tremendous innovation in the field of neural engineering, many challenges and opportunities remain at the frontiers of the neural interface in terms of both neurobiology and engineering. In this short-communication, we explore critical needs in the neural engineering field to overcome these challenges. Disentangling the complexities involved in the chronic neural interface problem requires simultaneous proficiency in multiple scientific and engineering disciplines. The critical component of advancing neural interface knowledge is to prepare the next wave of investigators who have simultaneous multi-disciplinary proficiencies with a diverse set of perspectives necessary to solve the chronic neural interface challenge.

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PAUGAM-MOISY, HÉLÈNE. « HOW TO MAKE GOOD USE OF MULTILAYER NEURAL NETWORKS ». Journal of Biological Systems 03, n^o 04 (décembre 1995) : 1177–91. http://dx.doi.org/10.1142/s0218339095001064.

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This article is a survey of recent advances on multilayer neural networks. The first section is a short summary on multilayer neural networks, their history, their architecture and their learning rule, the well-known back-propagation. In the following section, several theorems are cited, which present one-hidden-layer neural networks as universal approximators. The next section points out that two hidden layers are often required for exactly realizing d-dimensional dichotomies. Defining the frontier between one-hidden-layer and two-hidden-layer networks is still an open problem. Several bounds on the size of a multilayer network which learns from examples are presented and we enhance the fact that, even if all can be done with only one hidden layer, more often, things can be done better with two or more hidden layers. Finally, this assertion 'is supported by the behaviour of multilayer neural networks in two applications: prediction of pollution and odor recognition modelling.

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Zeng, Lingtao. « The Development of Image Classification Models Based on Computer Vision ». Highlights in Science, Engineering and Technology 34 (28 février 2023) : 430–34. http://dx.doi.org/10.54097/hset.v34i.5505.

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Image classification is a fundamental problem in computer vision, which deserves much attention in the past decade. To provide a comprehensive recall for the image classification task based on the deep learning algorithms, this paper first provides a brief history of computer vision and convolutional neural networks. Then, the current state of research and the direction of development of deep learning-based convolutional neural network models for image classification are examined. In addition, the basic model structure, convolution feature extraction, and pooling operations of standard and convolutional neural networks are also introduced. This paper summarizes the development of convolutional neural network models in recent years. In summary, based on the review of the development of image classification algorithms at home and abroad, the current mainstream image classification algorithms and frontier progress are summarized and analyzed, and the existing problems and future development directions of image classification are summarized and prospected. It can be concluded that deep learning-based methods has a great potential in this case.

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de Pedro-Carracedo, Javier, David Fuentes-Jimenez, Ana María Ugena et Ana Pilar Gonzalez-Marcos. « Transcending Conventional Biometry Frontiers : Diffusive Dynamics PPG Biometry ». Sensors 21, n^o 16 (23 août 2021) : 5661. http://dx.doi.org/10.3390/s21165661.

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This paper presents the first photoplethysmographic (PPG) signal dynamic-based biometric authentication system with a Siamese convolutional neural network (CNN). Our method extracts the PPG signal’s biometric characteristics from its diffusive dynamics, characterized by geometric patterns in the (p,q)-planes specific to the 0–1 test. PPG signal diffusive dynamics are strongly dependent on the vascular bed’s biostructure, unique to each individual. The dynamic characteristics of the PPG signal are more stable over time than its morphological features, particularly in the presence of psychosomatic conditions. Besides its robustness, our biometric method is anti-spoofing, given the complex nature of the blood network. Our proposal trains using a national research study database with 40 real-world PPG signals measured with commercial equipment. Biometric system results for input data, raw and preprocessed, are studied and compared with eight primary biometric methods related to PPG, achieving the best equal error rate (ERR) and processing times with a single attempt, among all of them.

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O’Sullivan-Greene, Elma, Levin Kuhlmann, Ewan S. Nurse, Dean R. Freestone, David B. Grayden, Mark Cook, Anthony Burkitt et Iven Mareels. « Probing to Observe Neural Dynamics Investigated with Networked Kuramoto Oscillators ». International Journal of Neural Systems 27, n^o 01 (8 novembre 2016) : 1650038. http://dx.doi.org/10.1142/s0129065716500386.

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The expansion of frontiers in neural engineering is dependent on the ability to track, detect and predict dynamics in neural tissue. Recent innovations to elucidate information from electrical recordings of brain dynamics, such as epileptic seizure prediction, have involved switching to an active probing paradigm using electrically evoked recordings rather than traditional passive measurements. This paper positions the advantage of probing in terms of information extraction, by using a coupled oscillator Kuramoto model to represent brain dynamics. While active probing performs better at observing underlying system synchrony in Kuramoto networks, especially in non-Gaussian measurement environments, the benefits diminish with increasing relative size of electrode spatial resolution compared to synchrony area. This suggests probing will be useful for improved characterization of synchrony for suitably dense electrode recordings.

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COPPOLA, EMERY, et FERENC SZIDAROVSZKY. « CONFLICT BETWEEN WATER SUPPLY AND ENVIRONMENTAL HEALTH RISK : A COMPUTATIONAL NEURAL NETWORK APPROACH ». International Game Theory Review 06, n^o 04 (décembre 2004) : 475–92. http://dx.doi.org/10.1142/s0219198904000319.

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A two-person conflict is analyzed, where a water company and a community are the players and water supply and health risk constitute the payoff functions. An aquifer's response to different pumping policies is estimated at points of interest by a computational neural network, which was trained using simulation data from MODFLOW, a large-scale complicated finite difference model. Using the weighting method, the Pareto frontier is first determined, and four particular resolution methodologies are applied. The numerical results are analyzed and compared.

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Botvinick, Matthew M., et Jonathan D. Cohen. « The Computational and Neural Basis of Cognitive Control : Charted Territory and New Frontiers ». Cognitive Science 38, n^o 6 (31 juillet 2014) : 1249–85. http://dx.doi.org/10.1111/cogs.12126.

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Wolstenholme, Adrian J., Alan S. Bowman et David B. Sattelle. « Frontiers in parasite neurobiology : parasite genomics, neural signalling and new targets for control ». Invertebrate Neuroscience 7, n^o 4 (23 novembre 2007) : 179–81. http://dx.doi.org/10.1007/s10158-007-0061-3.

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Topilko, P., G. Maro et P. Charnay. « Les cellules des capsules frontières — une niche des cellules souches neurales dans le système nerveux péripherique ». Revue Neurologique 163, n^o 12 (décembre 2007) : 1252–55. http://dx.doi.org/10.1016/s0035-3787(07)78414-x.

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Topilko, Piotr. « Les cellules des capsules frontières — Une niche de cellules souches neurales dans le système nerveux périphérique ». Bulletin de l'Académie Nationale de Médecine 191, n^o 7 (octobre 2007) : 1383–94. http://dx.doi.org/10.1016/s0001-4079(19)32960-7.

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Beck, Cesar, Thami Covatti Piaia et Maitê Cecília Fabbri Moro. « COGITO ERGO NON SUM : FREEDOM OF THOUGHT AND NEURAL DEVICES ». Revista Direito e Justiça : Reflexões Sociojurídicas 23, n^o 47 (26 décembre 2023) : 143–62. http://dx.doi.org/10.31512/rdj.v23i47.1508.

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This work analyzes the threats neurotechnologies can pose to protecting freedom of thought as a fundamental right and a right of psychic and intellectual integrity. Neurotechnologies have made it possible to advance knowledge about brain information, and continuous technological advances have led to a better quality of life for people with neurological diseases. This field of expertise and innovation has produced new treatments for various disorders. This revolution, however, implies a brain-machine relationship that allows access to profoundly private information since the brain is the last frontier of privacy. In this sense, this article reflects on the risks that implanted neural devices and invasive neurotechnologies may pose to fundamental human rights. It concludes that creating a new category of neurological rights, enshrined in the Federal Constitution, is essential to face the coming scenario.

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Adivarekar1, Pravin P., Amarnath Prabhakaran A, Sukhwinder Sharma, Divya P, Muniyandy Elangovan et Ravi Rastogi. « Automated machine learning and neural architecture optimization ». Scientific Temper 14, n^o 04 (27 décembre 2023) : 1345–51. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.42.

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Automated machine learning (AutoML) and neural architecture optimization (NAO) represent pivotal components in the landscape of machine learning and artificial intelligence. This paper extensively explores these domains, aiming to delineate their significance, methodologies, cutting-edge techniques, challenges, and emerging trends. AutoML streamlines and democratizes machine learning by automating intricate procedures, such as algorithm selection and hyperparameter tuning. Conversely, NAO automates the design of neural network architectures, a critical aspect for optimizing deep learning model performance. Both domains have made substantial advancements, significantly impacting research, industry practices, and societal applications. Through a series of experiments, classifier accuracy, NAO model selection based on hidden unit count, and learning curve analysis were investigated. The results underscored the efficacy of machine learning models, the substantial impact of architectural choices on test accuracy, and the significance of selecting an optimal number of training epochs for model convergence. These findings offer valuable insights into the potential and limitations of AutoML and NAO, emphasizing the transformative potential of automation and optimization within the machine learning field. Additionally, this study highlights the imperative for further research to explore synergies between AutoML and NAO, aiming to bridge the gap between model selection, architecture design, and hyperparameter tuning. Such endeavors hold promise in opening new frontiers in automated machine learning methodologies.

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Gori, M., A. Kuchler et A. Sperduti. « On the implementation of frontier-to-root tree automata in recursive neural networks ». IEEE Transactions on Neural Networks 10, n^o 6 (1999) : 1305–14. http://dx.doi.org/10.1109/72.809076.

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Schaedler, Maximilian, Christian Bluemm, Maxim Kuschnerov, Fabio Pittalà, Stefano Calabrò et Stephan Pachnicke. « Deep Neural Network Equalization for Optical Short Reach Communication ». Applied Sciences 9, n^o 21 (2 novembre 2019) : 4675. http://dx.doi.org/10.3390/app9214675.

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Nonlinear distortion has always been a challenge for optical communication due to the nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a second type of nonlinearities, which results from optical and electrical components. They become the dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments and memory effects using Volterra series. However, such Volterra equalizers are architecturally complex and their parametrization can be numerical unstable. This contribution proposes an alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated experimentally on coherent 88 Gbaud dual polarization 16QAM 600 Gb/s back-to-back measurements. The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6th orders at a target bit-error rate (BER) of 10 − 2 by 0.5 dB and 0.8 dB in optical signal-to-noise ratio (OSNR), respectively.

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Karakonstantis, Xenofon, et Efren Fernandez-Grande. « Advancing sound field analysis with physics-informed neural networks ». Journal of the Acoustical Society of America 154, n^o 4_supplement (1 octobre 2023) : A98. http://dx.doi.org/10.1121/10.0022920.

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This work introduces a method that employs physics-informed neural networks to reconstruct sound fields in diverse rooms, including both typical acoustically damped meeting rooms and more spaces of cultural significance, such as concert halls or theatres. The neural network is trained using a limited set of room impulse responses, integrating the expressive capacity of neural networks with the fundamental physics of sound propagation governed by the wave equation. Consequently, the network accurately represents sound fields within an aperture without requiring extensive measurements, regardless of the complexity of the sound field. Notably, our approach extends beyond sound pressure estimation and includes valuable vectorial quantities, such as particle velocity and intensity, resembling classical holography methods. Experimental results confirm the efficacy of the proposed approach, underscoring its reconstruction accuracy and computational efficiency. Moreover, by enabling the acquisition of sound field quantities in the time domain, which were previously challenging to obtain from measurements, our method opens up new frontiers for the analysis and comprehension of sound propagation phenomena in rooms.

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Walavalkar, Viraj. « Exploring the Frontiers of Artificial Intelligence : Advancements, Challenges, and Future Directions ». International Journal for Research in Applied Science and Engineering Technology 11, n^o 4 (30 avril 2023) : 1351–59. http://dx.doi.org/10.22214/ijraset.2023.50361.

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Abstract: Artificial intelligence (A.I.) is a multidisciplinary field that is ready to create a new revolution in the world by automating tasks and making decisions with the help of intelligent machines and thereby replacing human intelligence. This paper's objective is to make laypeople aware of the power of AI and to utilise upcoming technologies like ChatGPT, Claude, AI Copilot, etc. as tools. The paper will discuss fundamental and recent advances in artificial intelligence research, covering neural networks, robotics, computer vision, and reinforcement learning. Parallel to that, we focus on advantages, limitations, and the Al Control Problem while highlighting the distinctive benefits of emerging technology. We conclude with a description of a number of active research areas and suggestions for additional study

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Sukanya, Shyamasundar. « Frontiers in research on maternal diabetes-induced neural tube defects : Past, present and future ». World Journal of Diabetes 3, n^o 12 (2012) : 196. http://dx.doi.org/10.4239/wjd.v3.i12.196.

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Bourilkov, Dimitri. « Machine and deep learning applications in particle physics ». International Journal of Modern Physics A 34, n^o 35 (20 décembre 2019) : 1930019. http://dx.doi.org/10.1142/s0217751x19300199.

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The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced, and cutting-edge applications in the experimental and theoretical/phenomenological domains are highlighted. After describing the challenges in the application of these novel analysis techniques, the review concludes by discussing the interactions between physics and machine learning as a two-way street enriching both disciplines and helping to meet the present and future challenges of data-intensive science at the energy and intensity frontiers.

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García-Córdova, Francisco, Antonio Guerrero-González et Fernando Hidalgo-Castelo. « Anthropomorphic Tendon-Based Hands Controlled by Agonist–Antagonist Corticospinal Neural Network ». Sensors 24, n^o 9 (3 mai 2024) : 2924. http://dx.doi.org/10.3390/s24092924.

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This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.

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Fang, Wei, Zhenhao Zhu, Shuwei Zhu, Jun Sun, Xiaojun Wu et Zhichao Lu. « LoNAS : Low-Cost Neural Architecture Search Using a Three-Stage Evolutionary Algorithm [Research Frontier] ». IEEE Computational Intelligence Magazine 18, n^o 2 (mai 2023) : 78–93. http://dx.doi.org/10.1109/mci.2023.3245799.

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Zhang, Meiqi, Fang Li, Dongyu Wang, Xiaohong Ba et Zhan Liu. « Mapping Research Trends from 20 Years of Publications in Rhythmic Auditory Stimulation ». International Journal of Environmental Research and Public Health 20, n^o 1 (23 décembre 2022) : 215. http://dx.doi.org/10.3390/ijerph20010215.

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This study aims to create an all-around insight into the evolutions, status, and global trends of rhythmic auditory stimulation (RAS) research via enhanced bibliometric methods for the 2001–2020 time period. Articles concerning RAS were extracted from the Web of Science database. CiteSpace, Bibliometrix, VOSviewer, and Graphpad Prism were employed to analyze publication patterns and research trends. A total of 586 publications related to RAS between 2001 and 2020 were retrieved from the Web of Science database. The researcher Goswami U. made the greatest contribution to this field. The University of Toronto was the institution that published the most articles. Motor dysfunction, sensory perception, and cognition are the three major domains of RAS research. Neural tracking, working memory, and neural basis may be the latest research frontiers. This study reveals the publication patterns and topic trends of RAS based on the records published between 2001 and 2020. The insights obtained provided useful references for the future research and applications of RAS.

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Kirkpatrick, James, Brendan McMorrow, David H. P. Turban, Alexander L. Gaunt, James S. Spencer, Alexander G. D. G. Matthews, Annette Obika et al. « Pushing the frontiers of density functionals by solving the fractional electron problem ». Science 374, n^o 6573 (10 décembre 2021) : 1385–89. http://dx.doi.org/10.1126/science.abj6511.

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Improving DFT with deep learning In the past 30 years, density functional theory (DFT) has emerged as the most widely used electronic structure method to predict the properties of various systems in chemistry, biology, and materials science. Despite a long history of successes, state-of-the-art DFT functionals have crucial limitations. In particular, significant systematic errors are observed for charge densities involving mobile charges and spins. Kirkpatrick et al . developed a framework to train a deep neural network on accurate chemical data and fractional electron constraints (see the Perspective by Perdew). The resulting functional outperforms traditional functionals on thorough benchmarks for main-group atoms and molecules. The present work offers a solution to a long-standing critical problem in DFT and demonstrates the success of combining DFT with the modern machine-learning methodology. —YS

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Randjelovic, Branislav, Srdjan Ribar, Vojislav Mitic, Bojana Markovic, Hans Fecht et Branislav Vlahovic. « Artificial neural network applied on sintered BaTiO3-ceramic density ». Science of Sintering 54, n^o 4 (2022) : 425–38. http://dx.doi.org/10.2298/sos2204425r.

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It is very important to determine microstructure parameters of consolidated ceramic samples, because it opens new frontiers for further microelectronics miniaturization and integrations. Therefore, controlling, predicting and designing the ceramic materials? properties are the objectives in ceramic materials consolidating process, within the science of sintering. In order to calculate the precise values of desired microstructure parameter at the level of the grains? coating layers based on the measurements on the bulk samples, we applied the artificial neural networks, as a powerful mathematical tool for mapping input-output data. Input signals are propagated forward, as well as the adjustable coefficients that contribute the calculated output signal, denoted as error, which is propagated backwards and replaced by examined parameter. In our previous research, we used neural networks to calculate different electrophysical parameters at the nano level of the grain boundary, like relative capacitance, breakdown voltage or tangent loss, and now we extend the research on sintered material?s density calculation. Errors on the network output were substituted by different consolidated samples density values measured on the bulk, thus enabling the calculation of precise material?s density values between the layers. We performed the neural network theoretical experiments for different number of neurons in hidden layers, according to experimental ceramics material?s density of ?=5.4x103[kg/m3], but it opens the possibility for neural networks application within other density values, as well.

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Bickford, Paula C. « Frontiers in Neural Transplantation and Repair : A Special Issue Based on the 11th ASNTR Meeting ». Cell Transplantation 14, n^o 4 (avril 2005) : 171–72. http://dx.doi.org/10.3727/000000005783983151.

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