Zirconia-Supported ZnO Single Layer for Syngas Conversion Revealed from Machine-Learning Atomic Simulation

2021 ◽  
Vol 12 (13) ◽  
pp. 3328-3334
Author(s):  
Siyue Chen ◽  
Sicong Ma ◽  
Zhi-Pan Liu
Keyword(s):  
Machine Learning ◽  
Single Layer ◽  
Atomic Simulation ◽  
Syngas Conversion

scholarly journals Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Elena Goi ◽  
Xi Chen ◽  
Qiming Zhang ◽  
Benjamin P. Cumming ◽  
Steffen Schoenhardt ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Near Infrared ◽  
Single Layer ◽  
Infrared Region ◽  
Optical Devices ◽  
Medical Diagnostics ◽  
Oxide Semiconductor ◽  
Process Data ◽  
Neuron Density

AbstractOptical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide–semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm1,2, achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption3, sensing4, medical diagnostics5 and computing6,7.

scholarly journals Towards a Multi-Layered Phishing Detection

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4540
Author(s):  
Kieran Rendall ◽  
Antonia Nisioti ◽  
Alexios Mylonas
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Detection System ◽  
Single Layer ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Feature Sets ◽  
Phishing Attacks ◽  
Production Environments ◽  
Phishing Detection

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art.

Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide

2018 ◽  
Vol 20 (47) ◽  
pp. 30006-30020 ◽  
Author(s):  
Wenwen Li ◽  
Yasunobu Ando
Keyword(s):  
Machine Learning ◽  
Silicon Dioxide ◽  
Force Field ◽  
Computational Cost ◽  
High Accuracy ◽  
Simulation Approach ◽  
Learning Models ◽  
Atomic Simulation ◽  
Low Computational Cost ◽  
Machine Learning Models

Recently, the machine learning (ML) force field has emerged as a powerful atomic simulation approach because of its high accuracy and low computational cost.

scholarly journals Hydrological time series prediction by extreme machine learning and sparrow search algorithm

2021 ◽  
Author(s):  
Bao-fei Feng ◽  
Yin-shan Xu ◽  
Tao Zhang ◽  
Xiao Zhang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Search Algorithm ◽  
Time Series Prediction ◽  
Single Layer ◽  
Classification Problems ◽  
Water Resource System ◽  
Evaluation Indexes ◽  
Runoff Series ◽  
Prediction Problems

Abstract In general, accurate hydrological time series prediction information is of great significance for the rational planning and management of water resource system. Extreme learning machine (ELM) is an effective tool proposed for the single-layer feedforward neural network in the regression and classification problems. However, the standard ELM model falls into local minimum with a high probability in hydrological prediction problems since the randomly assigned parameters (like input-hidden weights and hidden biases) often remain unchanged at the learning process. For effectively improving the prediction accuracy, this paper develops a hybrid hydrological forecasting model where the emerging sparrow search algorithm (SSA) is firstly used to determine the satisfying parameter combinations of the ELM model, and then the Moore-Penrose generalized inverse method is chosen to analytically obtain the weight matrix between the hidden layer and output layer. The proposed method is used to forecast the long-term daily runoff series collected from three real-world hydrological stations in China. Based on several performance evaluation indexes, the results show that the proposed method outperforms several ELM variants optimized by other evolutionary algorithms in both training and testing phases. Hence, an effective evolutionary machine learning tool is developed for accurate hydrological time series forecasting. HIGHLIGHT Hydrologic forecasting, sparrow search algorithm, extreme machine learning.

scholarly journals Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning

2019 ◽  
Vol 3 (1) ◽  
pp. 10 ◽  
Author(s):  
Massimo Stella
Keyword(s):  
Machine Learning ◽  
Cognitive Task ◽  
Mental Lexicon ◽  
Single Layer ◽  
Human Cognition ◽  
Local Network ◽  
Conceptual System ◽  
Cognitive Computing ◽  
Complex Cognitive Task ◽  
Word Acquisition

Early language acquisition is a complex cognitive task. Recent data-informed approaches showed that children do not learn words uniformly at random but rather follow specific strategies based on the associative representation of words in the mental lexicon, a conceptual system enabling human cognitive computing. Building on this evidence, the current investigation introduces a combination of machine learning techniques, psycholinguistic features (i.e., frequency, length, polysemy and class) and multiplex lexical networks, representing the semantics and phonology of the mental lexicon, with the aim of predicting normative acquisition of 529 English words by toddlers between 22 and 26 months. Classifications using logistic regression and based on four psycholinguistic features achieve the best baseline cross-validated accuracy of 61.7% when half of the words have been acquired. Adding network information through multiplex closeness centrality enhances accuracy (up to 67.7%) more than adding multiplex neighbourhood density/degree (62.4%) or multiplex PageRank versatility (63.0%) or the best single-layer network metric, i.e., free association degree (65.2%), instead. Multiplex closeness operationalises the structural relevance of words for semantic and phonological information flow. These results indicate that the whole, global, multi-level flow of information and structure of the mental lexicon influence word acquisition more than single-layer or local network features of words when considered in conjunction with language norms. The highlighted synergy of multiplex lexical structure and psycholinguistic norms opens new ways for understanding human cognition and language processing through powerful and data-parsimonious cognitive computing approaches.

Emotion Recognition With Facial Expression Using Machine Learning for Social Network and Healthcare

2020 ◽  
pp. 273-282
Author(s):  
Anju Yadav ◽  
Venkatesh Gauri Shankar ◽  
Vivek Kumar Verma
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Facial Expression ◽  
Practical Importance ◽  
Single Layer ◽  
Expression Recognition ◽  
Emotional States ◽  
Learning Approaches ◽  
Novel Approach ◽  
Challenges And Opportunities

In this chapter, machine learning application on facial expression recognition (FER) is studied for seven emotional states (disgust, joy, surprise, anger, sadness, contempt, and fear) based on FER describing coefficient. FER has many practical importance in various area like social network, robotics, healthcare, etc. Further, a literature review of existing machine learning approaches for FER is discussed, and a novel approach for FER is given for static and dynamic images. Then the results are compared with the other existing approaches. The chapter also covers additional related issues of applications, various challenges, and opportunities in future FER. For security-based face detection systems that can identify an individual, in any form of expression he introduces himself. Doctors will use this system to find the intensity of illness or pain of a deaf and dumb patient. The proposed model is based on machine learning application with three types of prototypes, which are pre-trained model, single layer augmented model, and multi-layered augmented model, having a combined accuracy of approx. 99%.

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