scholarly journals Exploring Uncertainty Measures for Image-caption Embedding-and-retrieval Task

2021 ◽  
Vol 17 (2) ◽  
pp. 1-19
Author(s):  
Kenta Hama ◽  
Takashi Matsubara ◽  
Kuniaki Uehara ◽  
Jianfei Cai
Keyword(s):  
Deep Learning ◽  
Reliability Assessment ◽  
Model Averaging ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Retrieval Task ◽  
Uncertainty Measures ◽  
Consistent Performance ◽  
Learning Architectures ◽  
Image Caption

With the significant development of black-box machine learning algorithms, particularly deep neural networks, the practical demand for reliability assessment is rapidly increasing. On the basis of the concept that “Bayesian deep learning knows what it does not know,” the uncertainty of deep neural network outputs has been investigated as a reliability measure for classification and regression tasks. By considering an embedding task as a regression task, several existing studies have quantified the uncertainty of embedded features and improved the retrieval performance of cutting-edge models by model averaging. However, in image-caption embedding-and-retrieval tasks, well-known samples are not always easy to retrieve. This study shows that the existing method has poor performance in reliability assessment and investigates another aspect of image-caption embedding-and-retrieval tasks. We propose posterior uncertainty by considering the retrieval task as a classification task, which can accurately assess the reliability of retrieval results. The consistent performance of the two uncertainty measures is observed with different datasets (MS-COCO and Flickr30k), different deep-learning architectures (dropout and batch normalization), and different similarity functions. To the best of our knowledge, this is the first study to perform a reliability assessment on image-caption embedding-and-retrieval tasks.

scholarly journals Certain Investigations on the Application of Machine learning Algorithms and Deep Learning Architectures for Covid -19 Diagnosis

2021 ◽  
Author(s):  
Soundariya R.S. ◽  
◽  
Tharsanee R.M. ◽  
Vishnupriya B ◽  
Ashwathi R ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mathematical Models ◽  
Ct Scan ◽  
Virus Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Laboratory Test Results ◽  
Learning Architectures

Corona virus disease (Covid - 19) has started to promptly spread worldwide from April 2020 till date, leading to massive death and loss of lives of people across various countries. In accordance to the advices of WHO, presently the diagnosis is implemented by Reverse Transcription Polymerase Chain Reaction (RT- PCR) testing, that incurs four to eight hours’ time to process test samples and adds 48 hours to categorize whether the samples are positive or negative. It is obvious that laboratory tests are time consuming and hence a speedy and prompt diagnosis of the disease is extremely needed. This can be attained through several Artificial Intelligence methodologies for prior diagnosis and tracing of corona diagnosis. Those methodologies are summarized into three categories: (i) Predicting the pandemic spread using mathematical models (ii) Empirical analysis using machine learning models to forecast the global corona transition by considering susceptible, infected and recovered rate. (iii) Utilizing deep learning architectures for corona diagnosis using the input data in the form of X-ray images and CT scan images. When X-ray and CT scan images are taken into account, supplementary data like medical signs, patient history and laboratory test results can also be considered while training the learning model and to advance the testing efficacy. Thus the proposed investigation summaries the several mathematical models, machine learning algorithms and deep learning frameworks that can be executed on the datasets to forecast the traces of COVID-19 and detect the risk factors of coronavirus.

scholarly journals Malware Detection using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 1847-1853
Author(s):  
T. Shiva Rama Krishna
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Image Processing Technique ◽  
Feature Engineering ◽  
Public And Private ◽  
Static And Dynamic Analysis ◽  
Private Datasets ◽  
Learning Architectures

Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.

Deep Learning Architectures and Tools

2021 ◽  
pp. 55-75
Author(s):  
K. Bhargavi
Keyword(s):  
Machine Learning ◽  
Nervous System ◽  
Deep Learning ◽  
Information Processing ◽  
Learning Strategies ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Communication Pattern ◽  
Multiple Levels ◽  
Learning Architectures

Deep learning is one of the popular machine learning strategies that learns in a supervised or unsupervised manner by forming a cascade of multiple layers of non-linear processing units. It is inspired by the way of information processing and communication pattern of the typical biological nervous system. The deep learning algorithms learn through multiple levels of abstractions and hierarchy of concepts; as a result, it is found to be more efficient than the conventional non-deep machine learning algorithms. This chapter explains the basics of deep learning by highlighting the necessity of deep learning over non-deep learning. It also covers discussion on several recently developed deep learning architectures and popular tools available in market for deep learning, which includes Tensorflow, PyTorch, Keras, Caffe, Deeplearning4j, Pylearn2, Theano, CuDDN, CUDA-Convnet, and Matlab.

Image Caption Generation Using Deep Learning

2018 ◽  
Vol 06 (10) ◽  
pp. 53-55
Author(s):  
Sailee P. Pawaskar ◽  
J. A. Laxminarayana
Keyword(s):  
Deep Learning ◽  
Image Caption Generation ◽  
Image Caption

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4155
Author(s):  
Bulent Ayhan ◽  
Chiman Kwan
Keyword(s):  
Deep Learning ◽  
Noise Source ◽  
Simulated Data ◽  
Nuclear Material ◽  
Machine Learning Algorithms ◽  
Detector Response ◽  
Nuclear Materials ◽  
Ratio Estimation ◽  
Analysis Software ◽  
Detector Distance

Detecting nuclear materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, source-detector distance variations, and others. This paper presents new results on nuclear material identification and relative count contribution (also known as mixing ratio) estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep-learning-based machine learning algorithms were compared. Realistic simulated data using Gamma Detector Response and Analysis Software (GADRAS) were used in our comparative studies. It was observed that a deep learning approach is highly promising.

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