scholarly journals One-step regression and classification with cross-point resistive memory arrays

2020 ◽  
Vol 6 (5) ◽  
pp. eaay2378 ◽  
Author(s):  
Zhong Sun ◽  
Giacomo Pedretti ◽  
Alessandro Bricalli ◽  
Daniele Ielmini
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Resistive Memory ◽  
Cross Point ◽  
Data Intensive ◽  
One Step ◽  
Data Intensive Applications

Machine learning has been getting attention in recent years as a tool to process big data generated by the ubiquitous sensors used in daily life. High-speed, low-energy computing machines are in demand to enable real-time artificial intelligence processing of such data. These requirements challenge the current metal-oxide-semiconductor technology, which is limited by Moore’s law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures, and devices are thus strongly needed to accelerate data-intensive applications. Here, we show that a cross-point resistive memory circuit with feedback configuration can train traditional machine learning algorithms such as linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. One-step learning is further supported by simulations of the prediction of housing price in Boston and the training of a two-layer neural network for MNIST digit recognition.

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.

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

2020 ◽  
Vol 50 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Changwon Suh ◽  
Clyde Fare ◽  
James A. Warren ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
State Of The Art ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Computational Learning ◽  
Data Intensive ◽  
Research Activities ◽  
Materials Genome

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.

scholarly journals Feature-Based Opinion Mining and Managed Machine Learning with Sentiment Classification Models

2019 ◽  
Vol 9 (2) ◽  
pp. 3992-3998
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Intensive ◽  
Learning Tasks ◽  
Feature Based

Sentiment Analysis is individuals' opinions and feedbacks study towards a substance, which can be items, services, movies, people or events. The opinions are mostly expressed as remarks or reviews. With the social network, gatherings and websites, these reviews rose as a significant factor for the client’s decision to buy anything or not. These days, a vast scalable computing environment provides us with very sophisticated way of carrying out various data-intensive natural language processing (NLP) and machine-learning tasks to examine these reviews. One such example is text classification, a compelling method for predicting the clients' sentiment. In this paper, we attempt to center our work of sentiment analysis on movie review database. We look at the sentiment expression to order the extremity of the movie reviews on a size of 0(highly disliked) to 4(highly preferred) and perform feature extraction and ranking and utilize these features to prepare our multilabel classifier to group the movie review into its right rating. This paper incorporates sentiment analysis utilizing feature-based opinion mining and managed machine learning. The principle center is to decide the extremity of reviews utilizing nouns, verbs, and adjectives as opinion words. In addition, a comparative study on different classification approaches has been performed to determine the most appropriate classifier to suit our concern problem space. In our study, we utilized six distinctive machine learning algorithms – Naïve Bayes, Logistic Regression, SVM (Support Vector Machine), RF (Random Forest) KNN (K nearest neighbors) and SoftMax Regression.

scholarly journals A real-time fault diagnosis system for high-speed power system protection based on machine learning algorithms

2020 ◽  
Vol 10 (6) ◽  
pp. 6122
Author(s):  
Elmahdi Khoudry ◽  
Abdelaziz Belfqih ◽  
Tayeb Ouaderhman ◽  
Jamal Boukherouaa ◽  
Faissal Elmariami
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Power System ◽  
Real Time ◽  
High Speed ◽  
Learning Algorithms ◽  
Time Estimation ◽  
Machine Learning Algorithms ◽  
Diagnosis System ◽  
Fault Diagnosis System

This paper puts forward a real-time smart fault diagnosis system (SFDS) intended for high-speed protection of power system transmission lines. This system is based on advanced signal processing techniques, traveling wave theory results, and machine learning algorithms. The simulation results show that the SFDS can provide an accurate internal/external fault discrimination, fault inception time estimation, fault type identification, and fault location. This paper presents also the hardware requirements and software implementation of the SFDS.

scholarly journals Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition

2021 ◽  
Author(s):  
Dian Kesumapramudya Nurputra ◽  
Ahmad Kusumaatmadja ◽  
Mohamad Saifudin Hakim ◽  
Shidiq Nur Hidayat ◽  
Trisna Julian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Electronic Nose ◽  
Quantitative Polymerase Chain Reaction ◽  
Machine Learning Algorithms ◽  
Oxide Semiconductor ◽  
Support Vector ◽  
Detection Accuracy ◽  
Exhaled Breath ◽  
Laboratory Equipment ◽  
Linear Discriminant

Abstract Despite its high accuracy to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach possesses several limitations (e.g., the lengthy invasive procedure, the reagent availability, and the requirement of specialized laboratory, equipment, and trained staffs). We developed and employed a low-cost, noninvasive method to rapidly sniff out the coronavirus disease 2019 (COVID-19) based on a portable electronic nose (GeNose C19) integrating metal oxide semiconductor gas sensor array, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total number of 615 breath samples (i.e., 333 positive and 282 negative COVID-19 confirmed by RT-qPCR) obtained from 83 patients in two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis (LDA), support vector machine (SVM), stacked multilayer perceptron (MLP), and deep neural network (DNN)) were utilized to identify the top-performing pattern recognition methods and to obtain high system detection accuracy (88–95%), sensitivity (86–94%), specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.

scholarly journals Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia

2019 ◽  
Vol 8 (2S11) ◽  
pp. 542-546
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Selling Price ◽  
Prediction Problem ◽  
Real Dataset ◽  
Price Prediction

This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.

scholarly journals How to Utilize My App Reviews? A Novel Topics Extraction Machine Learning Schema for Strategic Business Purposes

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1310
Author(s):  
Ioannis Triantafyllou ◽  
Ioannis C. Drivas ◽  
Georgios Giannakopoulos
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Software Maintenance ◽  
Extraction Methods ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Case Scenario ◽  
Feature Extraction Method ◽  
Term Extraction ◽  
One Step

Acquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.

Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size

2019 ◽  
Vol 21 (9) ◽  
pp. 693-699 ◽  
Author(s):  
A. Alper Öztürk ◽  
A. Bilge Gündüz ◽  
Ozan Ozisik
Keyword(s):  
Machine Learning ◽  
Particle Size ◽  
High Speed ◽  
Mean Absolute Error ◽  
Mixing Time ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Aims and Objectives: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric. Materials and Methods: SLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated. Results: PS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods. Conclusion: Our findings present that machine learning methods can be highly useful for determining formulation parameters for further research.

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