scholarly journals An Evidence Quality Assessment Model for Cyber Security Policymaking

2018 ◽  
pp. 23-38
Author(s):  
Atif Hussain ◽  
Siraj Shaikh ◽  
Alex Chung ◽  
Sneha Dawda ◽  
Madeline Carr
Keyword(s):  
Quality Assessment ◽  
Cyber Security ◽  
Assessment Model ◽  
Evidence Quality

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

2018 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Assessment Model ◽  
Sensor Data ◽  
Edge Region ◽  
Kinect Sensor ◽  
Image Region ◽  
Filtering Technique

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

scholarly journals Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 72
Author(s):  
Li Wang ◽  
Yong Zhou ◽  
Qing Li ◽  
Tao Xu ◽  
Zhengxiang Wu ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Index System ◽  
Quality Index ◽  
Hubei Province ◽  
Machine Learning Algorithms ◽  
Assessment Model ◽  
Soil Conditions ◽  
The Mean ◽  
County Scale ◽  
Assessment Results

Constructing a scientific and quantitative quality-assessment model for farmland is important for understanding farmland quality, and can provide a theoretical basis and technical support for formulating rational and effective management policies and realizing the sustainable use of farmland resources. To more accurately reflect the systematic, complex, and differential characteristics of farmland quality, this study aimed to explore an intelligent farmland quality-assessment method that avoids the subjectivity of determining indicator weights while improving assessment accuracy. Taking Xiangzhou in Hubei Province, China, as the study area, 14 indicators were selected from four dimensions—terrain, soil conditions, socioeconomics, and ecological environment—to build a comprehensive assessment index system for farmland quality applicable to the region. A total of 1590 representative samples in Xiangzhou were selected, of which 1110 were used as training samples, 320 as test samples, and 160 as validation samples. Three models of entropy weight (EW), backpropagation neural network (BPNN), and random forest (RF) were selected for training, and the assessment results of farmland quality were output through simulations to compare their assessment accuracy and analyze the distribution pattern of farmland quality grades in Xiangzhou in 2018. The results showed the following: (1) The RF model for farmland quality assessment required fewer parameters, and could simulate the complex relationships between indicators more accurately and analyze each indicator’s contribution to farmland quality scientifically. (2) In terms of the average quality index of farmland, RF > BPNN > EW. The spatial patterns of the quality index from RF and BPNN were similar, and both were significantly different from EW. (3) In terms of the assessment results and precision characterization indicators, the assessment results of RF were more in line with realities of natural and socioeconomic development, with higher applicability and reliability. (4) Compared to BPNN and EW, RF had a higher data mining ability and training accuracy, and its assessment result was the best. The coefficient of determination (R2) was 0.8145, the mean absolute error (MAE) was 0.009, and the mean squared error (MSE) was 0.012. (5) The overall quality of farmland in Xiangzhou was higher, with a larger area of second- and third-grade farmland, accounting for 54.63%, and the grade basically conformed to the trend of positive distribution, showing an obvious pattern of geographical distribution, with overall high performance in the north-central part and low in the south. The distribution of farmland quality grades also varied widely among regions. This showed that RF was more suitable for the quality assessment of farmland with complex nonlinear characteristics. This study enriches and improves the index system and methodological research of farmland quality assessment at the county scale, and provides a basis for achieving a threefold production pattern of farmland quantity, quality, and ecology in Xiangzhou, while also serving as a reference for similar regions and countries.

scholarly journals Analysing IoT Cyber Risk for Estimating IoT Cyber Insurance

2019 ◽  
Author(s):  
Petar Radanliev ◽  
Rafael Mantilla Montalvo ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Stacy Cannady ◽  
...  
Keyword(s):  
Impact Assessment ◽  
Cyber Security ◽  
Industry 4.0 ◽  
Assessment Model ◽  
Theory Approach ◽  
Academic Literature ◽  
Cyber Insurance ◽  
Cyber Risk ◽  
Quantitative Impact ◽  
Grounded Theory Approach

This paper is focused on mapping the current evolution of Internet of Things (IoT) and its associated cyber risks for the Industry 4.0 (I4.0) sector. We report the results of a qualitative empirical study that correlates academic literature with 14 - I4.0 frameworks and initiatives. We apply the grounded theory approach to synthesise the findings from our literature review, to compare the cyber security frameworks and cyber security quantitative impact assessment models, with the world leading I4.0 technological trends. From the findings, we build a new impact assessment model of IoT cyber risk in Industry 4.0. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of economics impact assessment models for I4.0.

Improving the Security of Storage Systems

2019 ◽  
pp. 796-829
Author(s):  
Wasan Awad ◽  
Hanin Mohammed Abdullah
Keyword(s):  
Cyber Security ◽  
Storage Systems ◽  
Assessment Model ◽  
Security Threats ◽  
Risk Matrix ◽  
Security Risks ◽  
Security Systems ◽  
Security Vulnerabilities ◽  
File Storage ◽  
Local Storage

Developing security systems to protect the storage systems are needed. The main objective of this paper is to study the security of file storage server of an organization. Different kinds of security threats and a number of security techniques used to protect information will be examined. Thus, in this paper, an assessment plan for evaluating cyber security of local storage systems in organizations is proposed. The assessment model is based on the idea of cyber security domains and risk matrix. The proposed assessment model has been implemented on two prestigious and important organizations in the Kingdom of Bahrain. Storage systems of the assessed organizations found to have cyber security risks of different scales. This conclusion gives certainty to the fact that organizations are not capable of following the cyber security evolution and secure their storage systems from cyber security vulnerabilities and breaches. Organizations with local storage systems can improve the cyber security of their storage systems by applying certain techniques.

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