scholarly journals A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
MingQi Lv ◽  
Chao Huang ◽  
TieMing Chen ◽  
Ting Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Problem ◽  
Classification Accuracy ◽  
Mobile Apps ◽  
Semisupervised Learning ◽  
Mobile App ◽  
Semantic Meaning ◽  
Textual Information ◽  
Learning Framework

With the rapid growth of mobile Apps, it is necessary to classify the mobile Apps into predefined categories. However, there are two problems that make this task challenging. First, the name of a mobile App is usually short and ambiguous to reflect its real semantic meaning. Second, it is usually difficult to collect adequate labeled samples to train a good classifier when a customized taxonomy of mobile Apps is required. For the first problem, we leverage Web knowledge to enrich the textual information of mobile Apps. For the second problem, the mostly utilized approach is the semisupervised learning, which exploits unlabeled samples in a cotraining scheme. However, how to enhance the diversity between base learners to maximize the power of the cotraining scheme is still an open problem. Aiming at this problem, we exploit totally different machine learning paradigms (i.e., shallow learning and deep learning) to ensure a greater degree of diversity. To this end, this paper proposes Co-DSL, a collaborative deep and shallow semisupervised learning framework, for mobile App classification using only a few labeled samples and a large number of unlabeled samples. The experiment results demonstrate the effectiveness of Co-DSL, which could achieve over 85% classification accuracy by using only two labeled samples from each mobile App category.

Deep Learning Models for the Detection of Plant Leaf Diseases: A Systematic Review

2019 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Systems ◽  
Learning Models ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Learning Framework ◽  
Software Frameworks ◽  
Software Packages ◽  
Learning Frameworks

Machine learning systems use different algorithms to detect the diseases affecting the plant leaves. Nevertheless, selecting a suitable machine learning framework differs from study to study, depending on the features and complexity of the software packages. This paper introduces a taxonomic inspection of the literature in deep learning frameworks for the detection of plant leaf diseases. The objective of this study is to identify the dominating software frameworks in the literature for modelling machine learning plant leaf disease detecting systems.

Applicability of Machine Learning Methods on Mobile App Effort Estimation: Validation and Performance Evaluation

2020 ◽  
Vol 30 (01) ◽  
pp. 23-41 ◽  
Author(s):  
Mamta Pandey ◽  
Ratnesh Litoriya ◽  
Prateek Pandey
Keyword(s):  
Machine Learning ◽  
Cost Estimation ◽  
Web Applications ◽  
Fuzzy Inference ◽  
Mobile Apps ◽  
Mobile App ◽  
Effort Estimation ◽  
Operational Environment ◽  
Software Cost ◽  
Absolute Residual

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web applications may not be suitable for mobile app development. The objective of this paper is to propose a framework for mobile app project estimation. The research methodology adopted in this work is based on selecting different features of mobile apps from the SAMOA dataset. These features are later used as input vectors to the selected machine learning (ML) techniques. The results of this research experiment are measured in mean absolute residual (MAR). The experimental outcomes are then followed by the proposition of a framework to recommend an ML algorithm as the best match for superior effort estimation of a project in question. This framework uses the Mamdani-type fuzzy inference method to address the ambiguities in the decision-making process. The outcome of this work will particularly help mobile app estimators, development professionals, and industry at large to determine the required efforts in the projects accurately.

Deep learning enabled inorganic material generator

2020 ◽  
Vol 22 (46) ◽  
pp. 26935-26943
Author(s):  
Yashaswi Pathak ◽  
Karandeep Singh Juneja ◽  
Girish Varma ◽  
Masahiro Ehara ◽  
U. Deva Priyakumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Inorganic Material ◽  
Learning Framework

A machine learning framework that generates material compositions exhibiting properties desired by the user.

A Deep Learning framework for Interoperable Machine Learning

2021 ◽  
Author(s):  
Shakkeel Ahmed ◽  
Prakash Bisht ◽  
Ravi Mula ◽  
Soma S Dhavala
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Framework

scholarly journals A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets

2019 ◽  
Vol 9 (21) ◽  
pp. 4500 ◽  
Author(s):  
Phung ◽  
Rhee
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Complete Solution ◽  
Large Data ◽  
High Accuracy ◽  
Learning Approaches ◽  
Model Average ◽  
Image Patches

Research on clouds has an enormous influence on sky sciences and related applications, and cloud classification plays an essential role in it. Much research has been conducted which includes both traditional machine learning approaches and deep learning approaches. Compared with traditional machine learning approaches, deep learning approaches achieved better results. However, most deep learning models need large data to train due to the large number of parameters. Therefore, they cannot get high accuracy in case of small datasets. In this paper, we propose a complete solution for high accuracy of classification of cloud image patches on small datasets. Firstly, we designed a suitable convolutional neural network (CNN) model for small datasets. Secondly, we applied regularization techniques to increase generalization and avoid overfitting of the model. Finally, we introduce a model average ensemble to reduce the variance of prediction and increase the classification accuracy. We experiment the proposed solution on the Singapore whole-sky imaging categories (SWIMCAT) dataset, which demonstrates perfect classification accuracy for most classes and confirms the robustness of the proposed model.

scholarly journals All-optical machine learning using diffractive deep neural networks

Science ◽  
2018 ◽  
Vol 361 (6406) ◽  
pp. 1004-1008 ◽  
Author(s):  
Xing Lin ◽  
Yair Rivenson ◽  
Nezih T. Yardimci ◽  
Muhammed Veli ◽  
Yi Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Feature Detection ◽  
Learning Framework ◽  
Fashion Products ◽  
Complex Functions ◽  
All Optical ◽  
Computer Based ◽  
Classification Of Images

Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D2NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D2NNs.

scholarly journals Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences

2021 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Igor Ryazanov ◽  
Amanda T. Nylund ◽  
Debabrota Basu ◽  
Ida-Maja Hassellöv ◽  
Alexander Schliep
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Full Potential ◽  
Acoustic Data ◽  
Learning Framework ◽  
Machine Learning Methods ◽  
Deep Waters ◽  
The One ◽  
Relative Scarcity

Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences.

scholarly journals OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3047
Author(s):  
Xiaoyu Zhang ◽  
Yuting Xing ◽  
Kai Sun ◽  
Yike Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Decision Making ◽  
Personalised Medicine ◽  
High Dimensional ◽  
Survival Prediction ◽  
Omics Data ◽  
Learning Framework ◽  
Deep Embedding ◽  
Task Strategy

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called “the curse of dimensionality” in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.

scholarly journals Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

2021 ◽  
pp. 43-53
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Good Classification ◽  
Learning Architectures ◽  
Ecg Data

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

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