Design and Implementation of a Cognitive Tool to Detect Malicious Images Using the Smart Phone

2014 ◽  
Vol 6 (2) ◽  
pp. 30-40 ◽  
Author(s):  
Hiroyuki Nishiyama ◽  
Fumio Mizoguchi
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
User Interface ◽  
Service System ◽  
Smart Phone ◽  
Image Data ◽  
Support Vector ◽  
Cognitive Tool ◽  
Design And Implementation ◽  
Learning Data

In this study, the authors design a cognitive tool to detect malicious images using a smart phone. This tool can learn shot images taken with the camera of a smart phone and automatically classify the new image as a malicious image in the smart phone. To develop the learning and classifier tool, the authors implement an image analysis function and a learning and classifier function using a support vector machine (SVM) with the smart phone. With this tool, the user can collect image data with the camera of a smart phone, create learning data, and classify the new image data according to the learning data in the smart phone. In this study, the authors apply this tool to a user interface of a cosmetics recommendation service system and demonstrate its effectiveness by in reducing the load of the diagnosis server in this service and improving the user service.

scholarly journals Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

Solid Earth ◽  
2016 ◽  
Vol 7 (2) ◽  
pp. 481-492 ◽  
Author(s):  
Faisal Khan ◽  
Frieder Enzmann ◽  
Michael Kersten
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Image Data ◽  
Main Beam ◽  
Support Vector ◽  
Geological Samples ◽  
Data Set ◽  
Corrected Image ◽  
Phase Classification ◽  
Multi Phase

Abstract. Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

scholarly journals Pembangkitan Formulir Web Berdasarkan Metadata SQL dan Spesifikasi W3C

2018 ◽  
Vol 3 (2) ◽  
pp. 63
Author(s):  
Muhammad Fikry ◽  
Yusra Yusra ◽  
Taufik Hidayat
Keyword(s):  
Support Vector Machine ◽  
User Interface ◽  
User Acceptance ◽  
Black Box ◽  
Support Vector ◽  
Acceptance Test

Dalam membangun sistem informasi berbasis web, terdapat di dalamnya sebuah tahapan membangun formulir data isian sebagai representasi basis data didalam sistem informasi. Formulir menjadi jalan utama untuk memasukkan data kedalam basis data melalui sistem informasi. Dalam membangun formulir sistem informasi berbasis web, seorang user interface programmer akan merancang formulir dengan elemen-elemen HTML yang sesuai dengan struktur basis data. Penelitian ini membahas tentang bagaimana membangun aturan-aturan pembangkitan formulir dan mengimplementasikan aturan-aturan tersebut kedalam aplikasi pembangkit formulir. penelitian dilakukan terhadap standar bahasa SQL dan standar penulisan tag HTML, kemudian dilakukan pemetaan elemen SQL menjadi elemen formulir HTML sebagai acuan dalam membangun aturan-aturan membangkitkan formulir. Setelah itu dilakukan analisa terhadap model hubungan data pada RDBMS serta menganalisa dampaknya terhadap formulir. Hasil analisa aturan-aturan pembangkitan formulir akan diimplementasikan kedalam aplikasi pembangkit formulir berbasis web berdasarkan metadata SQL. Berdasarkan hasil pengujian yang dilakukan menggunakan Black Box dan User Acceptance Test, aplikasi pembangkit dapat dibangun dan berjalan dengan baik dalam membangkitkan formulir HTML.Kata kunci - Basis Data, HTML, Pembangkit Formulir Web, SQL, iraise, keluhan, klasifikasi, rapidminer, support vector machine 

Penentuan Luas Area Sawah Kabupaten Sleman Pada Citra Landsat 8 Operational Land Imager (OLI)

Respati ◽  
2018 ◽  
Vol 13 (3) ◽  
Author(s):  
Sulidar Fitri ◽  
Novi Nurjanah
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Support Vector Machine ◽  
Rice Field ◽  
Image Data ◽  
Rice Fields ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Learning Machine

INTISARITeknologi penginderaan jauh sangat baik dijadikan data pembuatan peta penggunaan lahan, karena kebutuhan pemetaan semakin tinggi terutama untuk mendeteksi perubahan penggunaan lahan terutama untuk penentuan luas area khususnya sawah di kabupaten Sleman. Untuk mendapatkan informasi luasan area sawah dari interpretasi citra landsat-8 OLI (Operational Land Imager) diperlukan metode khusus, terutama untuk pengolahan data citra penginderaan jauh secara digital. Salah satu metode pengolahan citra penginderaan jauh adalah metode Support Vector Machine (SVM). Metode SVM merupakan metode learning machine (Pembelajaran mesin) yang dapat mengklasifikasikan pola serta mengenali pola dari inputan atau contoh data yang diberikan dan juga termasuk ke dalam supervised learning. Hasil area sawah yang didapati dari citra Landsat 8 OLI dengan pengolahan metode SVM didapati berada di 18 kecamatan dala Kabupaten Sleman. Luasan tertinggi ada di kecamatan Ngaglik dengan 19,78 KM2 dan terendah di kecamatan Turi seluas 2,14 KM2. Nilai keseluruhan akurasi yang didapat untuk kelas lahan sawah dan area non sawah adalah adalah 53%.Kata kunci— Landsat-8 OLI, SVM, Data Citra, Geospasial, Luas Area Sawah ABSTRACTRemote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To get information about the area of the rice fields from the interpretation of Landsat-8 OLI (Operational Land Imager), special methods are needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method. The SVM method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The results of the rice field that were found from the Landsat 8 OLI image by processing the SVM method were found in 18 sub-districts in Sleman Regency. The highest area is in Ngaglik sub-district with 19.78 KM2 and the lowest in Turi sub-district is 2.14 KM2. The overall value of the accuracy obtained for the class of rice field and non-rice field is 53%.Kata kunci—  Landsat-8 OLI, SVM, Image Data, Geospatial, Area of Rice Fields

Cost Effective On-Site Fault Diagnosis Home Appliance Using a Smart Phone and Support Vector Machine

2019 ◽  
Author(s):  
Ji Min Baek ◽  
Kyeong Ha Lee ◽  
Seung Ho Lee ◽  
Ja Choon Koo
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Smart Phone ◽  
Poor Performance ◽  
Cost Effective ◽  
Consumer Electronics ◽  
Support Vector ◽  
Rotating Machines ◽  
Pass Filter ◽  
Rotating Machine

Abstract One of the common rotating machines of the consumer electronics might be a washing machine. The rotating machinery normally suffers mechanical failures even during daily operations that results in poor performance or shortening lifetime of the machine. Therefore, engineers have been interested in the earliest fault diagnosis of the rotating machine. Existing fault diagnosis methods for rotating machines have used fast fourier transform (FFT) method in frequency domain to detect abnormal frequency. However, it is difficult to diagnose using the FFT method if the normal frequency components of the rotating machines overlaps with the fault frequencies. In this paper, sets of acoustic signals generated by the washing machines are collected by using a smart phone in which an inexpensive microphone is equipped, and collected data are analyzed using a new algorithm, which combining the skewness, kurtosis, A-weighting filter, high-pass filter (HPF), and FFT. The analyzed data is applied to support vector machine (SVM) to determine defect existence. The proposed algorithm solves the disadvantages of the existing method and is accurate enough to discriminate the data collected by the cheap microphone of the smart phone.

scholarly journals Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Bin Ma ◽  
Zhaolong Wu ◽  
Shengyu Li ◽  
Ryan Benton ◽  
Dongqi Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sleep Apnea ◽  
Internet Of Things ◽  
Real Time ◽  
Early Warning ◽  
Smart Phone ◽  
Physiological Parameters ◽  
Classification Model ◽  
Support Vector ◽  
Obstructive Sleep

Abstract Background The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

Fuzzy Support Vector Machine for PolSAR Image Classification

2013 ◽  
Vol 639-640 ◽  
pp. 1162-1167 ◽  
Author(s):  
Hong Xia Ke ◽  
Guo Dong Liu ◽  
Guo Bing Pan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Image Data ◽  
Original Data ◽  
Total Power ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Polarimetric Synthetic Aperture Radar ◽  
Automatic Categorization ◽  
Svm Algorithm

Fully Polarimetric Synthetic Aperture Radar (PolSAR) image classification, with the complexity for its data’s scattering mechanism and statistical property, has expected to be performed by an automatic categorization. This paper presents a supervised method called Fuzzy support vector machine (FSVM), which is a variant of the SVM algorithm to classify the PolSAR image data. In order to take advantages of PolSAR data, five scattering features (entropy, total power, three Eigenvalues of Coherent Matrix: λ1,λ2,λ3) are input as original data space of the FSVM algorithm. The feasibility of this approach is examined by the JPL/AIRSAR PolSAR data. The classification results show that the proposed FSVM method has out-performed the SVM method.

Development of a support vector machine-based image analysis system for assessing the thyroid nodule malignancy risk on ultrasound

2005 ◽  
Vol 31 (11) ◽  
pp. 1451-1459 ◽  
Author(s):  
Stavros Tsantis ◽  
Dionisis Cavouras ◽  
Ioannis Kalatzis ◽  
Nikos Piliouras ◽  
Nikos Dimitropoulos ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
Thyroid Nodule ◽  
Support Vector ◽  
Image Analysis System ◽  
Malignancy Risk ◽  
Analysis System

Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data

2013 ◽  
Vol 22 (3) ◽  
pp. 306 ◽  
Author(s):  
Alfonso Alonso-Benito ◽  
Lara A. Arroyo ◽  
Manuel Arbelo ◽  
Pedro Hernández-Leal ◽  
Alejandro González-Calvo
Keyword(s):  
Support Vector Machine ◽  
Image Analysis ◽  
National Forest Inventory ◽  
Support Vector ◽  
Fire Behaviour ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Forest Fuel ◽  
Tenerife Island ◽  
Representative Area

Four classification algorithms have been assessed and compared with mapped forest fuel types from Terra-ASTER sensor images in a representative area of Tenerife Island (Canary Islands, Spain). A BEHAVE fuel-type map from 2002, together with field data also obtained in 2002 during the Third Spanish National Forest Inventory, was used as reference data. The BEHAVE fuel types of the reference dataset were first converted into the Fire Behaviour Fuel Types described by Scott and Burgan, taking into account the vegetation of the study area. Then, three pixel-based algorithms (Maximum Likelihood, Neural Network and Support Vector Machine) and an Object-Based Image Analysis were applied to classify the Scott and Burgan fire behaviour fuel types from an ASTER image from 3 March 2003. The performance of the algorithms tested was assessed and compared in terms of quantity disagreement and allocation disagreement. Within the pixel-based classifications, the best results were obtained from the Support Vector Machine algorithm, which showed an overall accuracy of 83%; 14% of disagreement was due to allocation and 3% to quantity disagreement. The Object-Based Image Analysis approach produced the most accurate maps, with an overall accuracy of 95%; 4% disagreement was due to allocation and 1% to quantity disagreement. The object-based classification achieved thus an overall accuracy of 12% above the best results obtained for the pixel-based algorithms tested. The incorporation of context information to the object-based classification allowed better identification of fuel types with similar spectral behaviour.

scholarly journals ImageM: a user-friendly interface for the processing of multi-dimensional images with Matlab

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 333
Author(s):  
David Legland ◽  
Marie-Françoise Devaux
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
User Interface ◽  
Image Data ◽  
Image Visualization ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Spatial Calibration ◽  
User Friendly ◽  
Efficient Software

Modern imaging devices provide a wealth of data often organized as images with many dimensions, such as 2D/3D, time and channel. Matlab is an efficient software solution for image processing, but it lacks many features facilitating the interactive interpretation of image data, such as a user-friendly image visualization, or the management of image meta-data (e.g. spatial calibration), thus limiting its application to bio-image analysis. The ImageM application proposes an integrated user interface that facilitates the processing and the analysis of multi-dimensional images within the Matlab environment. It provides a user-friendly visualization of multi-dimensional images, a collection of image processing algorithms and methods for analysis of images, the management of spatial calibration, and facilities for the analysis of multi-variate images. ImageM can also be run on the open source alternative software to Matlab, Octave. ImageM is freely distributed on GitHub: https://github.com/mattools/ImageM.

scholarly journals An Image Detection Method Based on Parameter Optimization of Support Vector Machine

2021 ◽  
Vol 15 ◽  
pp. 306-314
Author(s):  
Zhanshen Feng
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Parameter Optimization ◽  
Visual Processing ◽  
Image Data ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Image Detection

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.

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