scholarly journals A general end-to-end diagnosis framework for manufacturing systems

2019 ◽  
Vol 7 (2) ◽  
pp. 418-429 ◽  
Author(s):  
Ye Yuan ◽  
Guijun Ma ◽  
Cheng Cheng ◽  
Beitong Zhou ◽  
Huan Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Manufacturing Systems ◽  
Time Course ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Learning Techniques ◽  
General Data ◽  
End To End ◽  
Manufacturing Applications

Abstract The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.

Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

2022 ◽  
pp. 27-50
Author(s):  
Rajalaxmi Prabhu B. ◽  
Seema S.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Model Building ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Large Data Sets ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Techniques ◽  
Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

scholarly journals Yuel: Compound-Protein Interaction Prediction with High Generalizability

2021 ◽  
Author(s):  
Jian Wang ◽  
Nikolay V Dokholyan
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Small Molecules ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Data Sets ◽  
Interaction Prediction ◽  
Learning Techniques ◽  
Protein Interaction Prediction ◽  
High Prediction

In recent years, numerous structure-free deep-learning-based neural networks have emerged aiming to predict compound-protein interactions for drug virtual screening. Although these methods show high prediction accuracy in their own tests, we find that they are not generalizable to predict interactions between unknown proteins and unknown small molecules, thus hindering the utilization of state-of-the-art deep learning techniques in the field of virtual screening. In our work, we develop a compound-protein interaction predictor, YueL, which can predict compound-protein interactions with high generalizability. Upon comprehensive tests on various data sets, we find that YueL has the ability to predict interactions between unknown compounds and unknown proteins. We anticipate our work can motivate broad application of deep learning techniques for drug virtual screening to supersede the traditional docking and cheminformatics methods.

scholarly journals Training confounder-free deep learning models for medical applications

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Qingyu Zhao ◽  
Ehsan Adeli ◽  
Kilian M. Pohl
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Bone Age ◽  
Magnetic Resonance Images ◽  
Data Sets ◽  
Large Set ◽  
Learning Models ◽  
End To End ◽  
The Impact ◽  
The Relationship

AbstractThe presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variables (e.g., diagnosis). Improper modeling of those relationships often results in spurious and biased associations. Traditional machine learning and statistical models minimize the impact of confounders by, for example, matching data sets, stratifying data, or residualizing imaging measurements. Alternative strategies are needed for state-of-the-art deep learning models that use end-to-end training to automatically extract informative features from large set of images. In this article, we introduce an end-to-end approach for deriving features invariant to confounding factors while accounting for intrinsic correlations between the confounder(s) and prediction outcome. The method does so by exploiting concepts from traditional statistical methods and recent fair machine learning schemes. We evaluate the method on predicting the diagnosis of HIV solely from Magnetic Resonance Images (MRIs), identifying morphological sex differences in adolescence from those of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA), and determining the bone age from X-ray images of children. The results show that our method can accurately predict while reducing biases associated with confounders. The code is available at https://github.com/qingyuzhao/br-net.

scholarly journals Integration of Blockchain, IoT and Machine Learning for Multistage Quality Control and Enhancing Security in Smart Manufacturing

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1467 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Manufacturing Systems ◽  
True Positive Rate ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Control Approach ◽  
Learning Techniques ◽  
Positive Rate ◽  
Smart Manufacturing Systems

Smart manufacturing systems are growing based on the various requests for predicting the reliability and quality of equipment. Many machine learning techniques are being examined to that end. Another issue which considers an important part of industry is data security and management. To overcome the problems mentioned above, we applied the integrated methods of blockchain and machine learning to secure system transactions and handle a dataset to overcome the fake dataset. To manage and analyze the collected dataset, big data techniques were used. The blockchain system was implemented in the private Hyperledger Fabric platform. Similarly, the fault diagnosis prediction aspect was evaluated based on the hybrid prediction technique. The system’s quality control was evaluated based on non-linear machine learning techniques, which modeled that complex environment and found the true positive rate of the system’s quality control approach.

scholarly journals Deep learning in cancer diagnosis, prognosis and treatment selection

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Khoa A. Tran ◽  
Olga Kondrashova ◽  
Andrew Bradley ◽  
Elizabeth D. Williams ◽  
John V. Pearson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Diagnosis ◽  
Cancer Biology ◽  
Large Data ◽  
Data Sets ◽  
Precision Oncology ◽  
Data Types ◽  
Early Results ◽  
Learning Techniques ◽  
Rich Data

AbstractDeep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

scholarly journals The Application of Cognitive IoT for Smart Manufacturing

2020 ◽  
Vol 9 (3) ◽  
pp. 788-792
Keyword(s):  
Paradigm Shift ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Major Goal ◽  
Knowledge Accumulation ◽  
Learning Techniques ◽  
Manufacturing Sectors ◽  
Smart Manufacturing Systems ◽  
Cognitive Internet Of Things ◽  
Open Issues

In recent times, novel paradigms based on cognitive manufacturing services are evolving. This paradigm shift is conveyed by integrating manufacturing assets with the latest and enhanced methods and technologies. However, today’s manufacturing systems are facing various challenges, that is the researcher believed that existing technologies and tools specifically, IoT, existing learning techniques and learning systems, lack enough cognitive based intelligence and cannot achieve the expected enhancements and smart manufacturing developments. In Light of this assessing the advances in manufacturing sectors was the major goal of this work and to identify the cuuent issues and challenches in manufacturing systems being Cognitive and or smart Afterward, we assessed the research challenges and open issues and facilitate knowledge accumulation in efficiently in the applications of Cognitive Internet of Things (CIoT) for smart manufacturing systems.

scholarly journals Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

2019 ◽  
Author(s):  
Ellen M. Ditria ◽  
Sebastian Lopez-Marcano ◽  
Michael K. Sievers ◽  
Eric L. Jinks ◽  
Christopher J. Brown ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Fish Assemblages ◽  
Aquatic Ecology ◽  
Video Data ◽  
Fish Abundance ◽  
Data Sets ◽  
Seagrass Meadows ◽  
Learning Techniques ◽  
Better Than

AbstractAquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer’s performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.

scholarly journals Visual Explanation for Identification of the Brain Bases for Developmental Dyslexia on fMRI Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Laura Tomaz Da Silva ◽  
Nathalia Bianchini Esper ◽  
Duncan D. Ruiz ◽  
Felipe Meneguzzi ◽  
Augusto Buchweitz
Keyword(s):  
Deep Learning ◽  
Brain Imaging ◽  
Developmental Dyslexia ◽  
Brain Regions ◽  
Data Sets ◽  
Learning Approaches ◽  
Imaging Data ◽  
Learning Techniques ◽  
Brain Imaging Data

Problem: Brain imaging studies of mental health and neurodevelopmental disorders have recently included machine learning approaches to identify patients based solely on their brain activation. The goal is to identify brain-related features that generalize from smaller samples of data to larger ones; in the case of neurodevelopmental disorders, finding these patterns can help understand differences in brain function and development that underpin early signs of risk for developmental dyslexia. The success of machine learning classification algorithms on neurofunctional data has been limited to typically homogeneous data sets of few dozens of participants. More recently, larger brain imaging data sets have allowed for deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Indeed, deep learning techniques can provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. The adoption of deep learning approaches allows for incremental improvements in classification performance of larger functional brain imaging data sets, but still lacks diagnostic insights about the underlying brain mechanisms associated with disorders; moreover, a related challenge involves providing more clinically-relevant explanations from the neural features that inform classification.Methods: We target this challenge by leveraging two network visualization techniques in convolutional neural network layers responsible for learning high-level features. Using such techniques, we are able to provide meaningful images for expert-backed insights into the condition being classified. We address this challenge using a dataset that includes children diagnosed with developmental dyslexia, and typical reader children.Results: Our results show accurate classification of developmental dyslexia (94.8%) from the brain imaging alone, while providing automatic visualizations of the features involved that match contemporary neuroscientific knowledge (brain regions involved in the reading process for the dyslexic reader group and brain regions associated with strategic control and attention processes for the typical reader group).Conclusions: Our visual explanations of deep learning models turn the accurate yet opaque conclusions from the models into evidence to the condition being studied.

scholarly journals Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3949 ◽  
Author(s):  
Francisco Arellano-Espitia ◽  
Miguel Delgado-Prieto ◽  
Victor Martinez-Viol ◽  
Juan Jose Saucedo-Dorantes ◽  
Roque Alfredo Osornio-Rios
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Operating Conditions ◽  
Smart Manufacturing ◽  
Learning Technology ◽  
Electromechanical Systems ◽  
Monitoring Strategies ◽  
Complex Electromechanical Systems

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

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