An Ensemble-based Supervised Machine Learning Framework for Android Ransomware Detection

With latest development in technology, the usage of smartphones to fulfill day-to-day requirements has been increased. The Android-based smartphones occupy the largest market share among other mobile operating systems. The hackers are continuously keeping an eye on Android-based smartphones by creating malicious apps housed with ransomware functionality for monetary purposes. Hackers lock the screen and/or encrypt the documents of the victim’s Android based smartphones after performing ransomware attacks. Thus, in this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) reduce the dimensionality of the features, (3) employ an ensemble learning model to detect Android ransomware, and (4) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results reveal that the proposed framework detects Android ransomware by achieving an accuracy of 99.67% with Random Forest ensemble model. After reducing the dimensionality of the features with principal component analysis technique; the Logistic Regression model took least time to execute on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) in 41 milliseconds and 50 milliseconds respectively

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Implementation of Airy function using Graphics Processing Unit (GPU)

ITM Web of Conferences ◽

10.1051/itmconf/20203203052 ◽

2020 ◽

Vol 32 ◽

pp. 03052

Author(s):

Yugesh C. Keluskar ◽

Megha M. Navada ◽

Chaitanya S. Jage ◽

Navin G. Singhaniya

Keyword(s):

Graphics Processing Unit ◽

Special Functions ◽

Airy Function ◽

Processing Unit ◽

Mathematical Functions ◽

Central Processing ◽

Cpu Time ◽

Real Domain ◽

Time Required ◽

Graphics Processing

Special mathematical functions are an integral part of Fractional Calculus, one of them is the Airy function. But it’s a gruelling task for the processor as well as system that is constructed around the function when it comes to evaluating the special mathematical functions on an ordinary Central Processing Unit (CPU). The Parallel processing capabilities of a Graphics processing Unit (GPU) hence is used. In this paper GPU is used to get a speedup in time required, with respect to CPU time for evaluating the Airy function on its real domain. The objective of this paper is to provide a platform for computing the special functions which will accelerate the time required for obtaining the result and thus comparing the performance of numerical solution of Airy function using CPU and GPU.

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A Line Heat Input Model for Additive Manufacturing

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4033662 ◽

2016 ◽

Vol 138 (11) ◽

Cited By ~ 24

Author(s):

Jeff Irwin ◽

P. Michaleris

Keyword(s):

Additive Manufacturing ◽

Heat Source ◽

Heat Input ◽

Computational Time ◽

Processing Unit ◽

Powder Bed ◽

Central Processing ◽

Time Increment ◽

Input Model ◽

Time Required

A line input (LI) model has been developed, which makes the accurate modeling of powder bed processes more computationally efficient. Goldak's ellipsoidal model has been used extensively to model heat sources in additive manufacturing (AM), including lasers and electron beams. To accurately model the motion of the heat source, the simulation time increments must be small enough such that the source moves a distance smaller than its radius over the course of each increment. When the source radius is small and its velocity is large, a strict condition is imposed on the size of time increments regardless of any stability criteria. In powder bed systems, where radii of 0.1 mm and velocities of 500 mm/s are typical, a significant computational burden can result. The line heat input model relieves this burden by averaging the heat source over its path. This model allows the simulation of an entire heat source scan in just one time increment. However, such large time increments can lead to inaccurate results. Instead, the scan is broken up into several linear segments, each of which is applied in one increment. In this work, time increments are found that yield accurate results (less than 10% displacement error) and require less than 1/10 of the central processing unit (CPU) time required by Goldak's moving source model. A dimensionless correlation is given that can be used to determine the necessary time increment size that will greatly decrease the computational time required for any powder bed simulation while maintaining accuracy.

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Numerical simulation of flattened heat pipe with double heat sources for CPU and GPU cooling application in laptop computers

Journal of Computational Design and Engineering ◽

10.1093/jcde/qwaa091 ◽

2020 ◽

Author(s):

Wisoot Sanhan ◽

Kambiz Vafai ◽

Niti Kammuang-Lue ◽

Pradit Terdtoon ◽

Phrut Sakulchangsatjatai

Keyword(s):

Experimental Data ◽

Heat Pipe ◽

Graphics Processing Unit ◽

Processing Unit ◽

Heat Sources ◽

Final Thickness ◽

Laptop Computers ◽

Central Processing ◽

Graphics Processing ◽

Good Agreement

Abstract An investigation of the effect of the thermal performance of the flattened heat pipe on its double heat sources acting as central processing unit and graphics processing unit in laptop computers is presented in this work. A finite element method is used for predicting the flattening effect of the heat pipe. The cylindrical heat pipe with a diameter of 6 mm and the total length of 200 mm is flattened into three final thicknesses of 2, 3, and 4 mm. The heat pipe is placed under a horizontal configuration and heated with heater 1 and heater 2, 40 W in combination. The numerical model shows good agreement compared with the experimental data with the standard deviation of 1.85%. The results also show that flattening the cylindrical heat pipe to 66.7 and 41.7% of its original diameter could reduce its normalized thermal resistance by 5.2%. The optimized final thickness or the best design final thickness for the heat pipe is found to be 2.5 mm.

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A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7120472 ◽

2018 ◽

Vol 7 (12) ◽

pp. 472 ◽

Cited By ~ 1

Author(s):

Bo Wan ◽

Lin Yang ◽

Shunping Zhou ◽

Run Wang ◽

Dezhi Wang ◽

...

Keyword(s):

Road Network ◽

Large Scale ◽

Graphics Processing Unit ◽

Road Networks ◽

Processing Unit ◽

Data Partition ◽

Matching Method ◽

The Road ◽

Central Processing ◽

Relaxation Matching

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

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The prototype device for non-invasive diagnosis of arteriovenous fistula condition using machine learning methods

Scientific Reports ◽

10.1038/s41598-020-72336-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Marcin Grochowina ◽

Lucyna Leniowska ◽

Agnieszka Gala-Błądzińska

Keyword(s):

Machine Learning ◽

Arteriovenous Fistula ◽

Low Cost ◽

Principal Component ◽

Special Kind ◽

Classification Model ◽

Supervised Machine Learning ◽

Signal Acquisition ◽

Non Invasive ◽

Prototype Device

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.

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Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Information ◽

10.3390/info11040193 ◽

2020 ◽

Vol 11 (4) ◽

pp. 193 ◽

Cited By ~ 7

Author(s):

Sebastian Raschka ◽

Joshua Patterson ◽

Corey Nolet

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Science ◽

Gpu Computing ◽

Graphics Processing Unit ◽

General Purpose ◽

Processing Unit ◽

The Core ◽

Critical Components ◽

High Level

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.

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Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision

Symmetry ◽

10.3390/sym11040585 ◽

2019 ◽

Vol 11 (4) ◽

pp. 585

Author(s):

Yufei Wu ◽

Xiaofei Ruan ◽

Yu Zhang ◽

Huang Zhou ◽

Shengyu Du ◽

...

Keyword(s):

Real Time ◽

Pose Estimation ◽

Graphics Processing Unit ◽

Parallel Execution ◽

Processing Unit ◽

Network Efficiency ◽

Hand Pose Estimation ◽

Central Processing ◽

Deployment Optimization ◽

Hand Pose

The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the parallel execution capability of central processing units (CPUs). We conduct experiments on NYU and ICVL datasets and develop a demo1 using the RealSense camera. Experimental results show our lightweight network achieves an average running time of 32 ms (31.3 FPS, the original is 22.7 FPS) before deployment optimization. Meanwhile, the model is only about half parameters size of the original one with 11.9 mm mean joint error. After the further optimization with OpenVINO, the optimized model can run at 56 FPS on CPUs in contrast to 44 FPS running on a graphics processing unit (GPU) (Tensorflow) and it can achieve the real-time goal.

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A GPU-based 2D shallow water quality model

Journal of Hydroinformatics ◽

10.2166/hydro.2020.030 ◽

2020 ◽

Vol 22 (5) ◽

pp. 1182-1197

Author(s):

Geovanny Gordillo ◽

Mario Morales-Hernández ◽

I. Echeverribar ◽

Javier Fernández-Pato ◽

Pilar García-Navarro

Keyword(s):

Water Quality ◽

Shallow Water ◽

Graphics Processing Unit ◽

Temporal Distribution ◽

Water Quality Model ◽

Quality Analysis ◽

Test Case ◽

Processing Unit ◽

Quality Model ◽

Central Processing

Abstract In this study, a 2D shallow water flow solver integrated with a water quality model is presented. The interaction between the main water quality constituents included is based on the Water Quality Analysis Simulation Program. Efficiency is achieved by computing with a combination of a Central Processing Unit (CPU) and a Graphics Processing Unit (GPU) device. This technique is intended to provide robust and accurate simulations with high computation speedups with respect to a single-core CPU in real events. The proposed numerical model is evaluated in cases that include the transport and reaction of water quality components over irregular bed topography and dry–wet fronts, verifying that the numerical solution in these situations conserves the required properties (C-property and positivity). The model can operate in any steady or unsteady form allowing an efficient assessment of the environmental impact of water flows. The field data from an unsteady river reach test case are used to show that the model is capable of predicting the measured temporal distribution of dissolved oxygen and water temperature, proving the robustness and computational efficiency of the model, even in the presence of noisy signals such as wind speed.

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Acceleration of a CFD Code with a GPU

Scientific Programming ◽

10.1155/2010/564806 ◽

2010 ◽

Vol 18 (3-4) ◽

pp. 193-201 ◽

Cited By ~ 11

Author(s):

Dennis C. Jespersen

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Graphics Processing Unit ◽

Computational Time ◽

Processing Unit ◽

Graphics Processing ◽

Work Done

The Computational Fluid Dynamics code OVERFLOW includes as one of its solver options an algorithm which is a fairly small piece of code but which accounts for a significant portion of the total computational time. This paper studies some of the issues in accelerating this piece of code by using a Graphics Processing Unit (GPU). The algorithm needs to be modified to be suitable for a GPU and attention needs to be given to 64-bit and 32-bit arithmetic. Interestingly, the work done for the GPU produced ideas for accelerating the CPU code and led to significant speedup on the CPU.

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Implementation of algebraic procedures on the GPU using CUDA architecture on the example of generalized eigenvalue problem

Open Computer Science ◽

10.1515/comp-2016-0006 ◽

2016 ◽

Vol 6 (1) ◽

pp. 79-90

Author(s):

Łukasz Syrocki ◽

Grzegorz Pestka

Keyword(s):

Eigenvalue Problem ◽

Graphics Processing Unit ◽

Generalized Eigenvalue Problem ◽

Processing Unit ◽

Graphics Processors ◽

Central Processing ◽

Generalized Eigenvalue ◽

Cuda Technology ◽

Cuda Architecture ◽

High Level

AbstractThe ready to use set of functions to facilitate solving a generalized eigenvalue problem for symmetric matrices in order to efficiently calculate eigenvalues and eigenvectors, using Compute Unified Device Architecture (CUDA) technology from NVIDIA, is provided. An integral part of the CUDA is the high level programming environment enabling tracking both code executed on Central Processing Unit and on Graphics Processing Unit. The presented matrix structures allow for the analysis of the advantages of using graphics processors in such calculations.

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