Missed cancer and visual search of mammograms: what feature based machine-learning can tell us that deep-convolution learning cannot

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

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Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Sensors ◽

10.3390/s19143115 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3115 ◽

Cited By ~ 1

Author(s):

Yang Wei ◽

Hao Wang ◽

Kim Fung Tsang ◽

Yucheng Liu ◽

Chung Kit Wu ◽

...

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Learning Algorithm ◽

Rbf Neural Network ◽

Health Assessment ◽

Sensor Data ◽

Machine Learning Algorithm ◽

Tree Health ◽

Environmental Feature ◽

Feature Based

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

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Automatic Pulmonary Nodule Detection Applying Deep Learning or Machine Learning Algorithms to the LIDC-IDRI Database: A Systematic Review

Diagnostics ◽

10.3390/diagnostics9010029 ◽

2019 ◽

Vol 9 (1) ◽

pp. 29 ◽

Cited By ~ 20

Author(s):

Lea Pehrson ◽

Michael Nielsen ◽

Carsten Ammitzbøl Lauridsen

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Deep Learning ◽

Machine Learning Algorithms ◽

Ct Scans ◽

Lung Nodules ◽

Original Research ◽

Feature Based ◽

High Level ◽

Meta Analyses

The aim of this study was to provide an overview of the literature available on machine learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection (LIDC-IDRI) database as a tool for the optimization of detecting lung nodules in thoracic CT scans. This systematic review was compiled according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles concerning algorithms applied to the LIDC-IDRI database were included. The initial search yielded 1972 publications after removing duplicates, and 41 of these articles were included in this study. The articles were divided into two subcategories describing their overall architecture. The majority of feature-based algorithms achieved an accuracy >90% compared to the deep learning (DL) algorithms that achieved an accuracy in the range of 82.2%–97.6%. In conclusion, ML and DL algorithms are able to detect lung nodules with a high level of accuracy, sensitivity, and specificity using ML, when applied to an annotated archive of CT scans of the lung. However, there is no consensus on the method applied to determine the efficiency of ML algorithms.

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