scholarly journals Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tao Zhou ◽  
Huiling Lu ◽  
Junjie Zhang ◽  
Hongbin Shi
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Pulmonary Nodules ◽  
Image Feature ◽  
Ct Image ◽  
3 Dimensional ◽  
Detection Model ◽  
Proposed Model ◽  
Feature Level Fusion ◽  
Level Fusion

In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups’ comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.

A new approach of attribute reduction of rough sets based on soft metric

2020 ◽  
Vol 39 (3) ◽  
pp. 4473-4489
Author(s):  
H.I. Mustafa ◽  
O.A. Tantawy
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Data Set ◽  
Decision Systems ◽  
Proposed Model ◽  
Processing Step ◽  
Important Processing

Attribute reduction is considered as an important processing step for pattern recognition, machine learning and data mining. In this paper, we combine soft set and rough set to use them in applications. We generalize rough set model and introduce a soft metric rough set model to deal with the problem of heterogeneous numerical feature subset selection. We construct a soft metric on the family of knowledge structures based on the soft distance between attributes. The proposed model will degrade to the classical one if we specify a zero soft real number. We also provide a systematic study of attribute reduction of rough sets based on soft metric. Based on the constructed metric, we define co-information systems and consistent co-decision systems, and we provide a new method of attribute reductions of each system. Furthermore, we present a judgement theorem and discernibility matrix associated with attribute of each type of system. As an application, we present a case study from Zoo data set to verify our theoretical results.

A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

2021 ◽  
pp. 119-143
Author(s):  
N. Syed Siraj Ahmed ◽  
Debi Prasanna Acharjya
Keyword(s):  
Intrusion Detection ◽  
Rough Set ◽  
Equivalence Relations ◽  
Detection Model ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Proposed Model ◽  
Topology Changes ◽  
The Universe

The topology changes randomly and dynamically in a mobile adhoc network (MANET). The composite characteristics of MANETs makes it exposed to interior and exterior attacks. Avoidance support techniques like authentication and encryption are appropriate to prevent attacks in MANETs. Thus, an authoritative intrusion detection model is required to prevent from attacks. These attacks can be at either the layers present in the network or can be of a general attack. Many models have been developed for the detection of intrusion and detection. These models aim at any one of the layer present in the network. Therefore, effort has been made to consider either the layers for the detection of intrusion and detection. This article uses a multigranular rough set (MGRS) for the detection of intrusion and detection in MANET. The advantage of MGRS is that it can aim at either the layers present in the network simultaneously by using multiple equivalence relations on the universe. The proposed model is compared with many traditional models and attained higher accuracy.

scholarly journals Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data

2019 ◽  
Vol 11 (3) ◽  
pp. 620 ◽  
Author(s):  
Wenbing Chang ◽  
Xinglong Yuan ◽  
Yalong Wu ◽  
Shenghan Zhou ◽  
Jingsong Lei ◽  
...  
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Mixed Integer Linear Programming ◽  
Rough Set Theory ◽  
Mixed Integer ◽  
Quality Level ◽  
Proposed Model

The purpose of this paper is to establish a decision-making system for assembly clearance parameters and machine quality level by analyzing the data of assembly clearance parameters of diesel engine. Accordingly, we present an extension of the rough set theory based on mixed-integer linear programming (MILP) for rough set-based classification (MILP-FRST). Traditional rough set theory has two shortcomings. First, it is sensitive to noise data, resulting in a low accuracy of decision systems based on rough sets. Second, in the classification problem based on rough sets, the attributes cannot be automatically determined. MILP-FRST has the advantages of MILP in resisting noisy data and has the ability to select attributes flexibly and automatically. In order to prove the validity and advantages of the proposed model, we used the machine quality data and assembly clearance data of 29 diesel engines of a certain type to validate the proposed model. Experiments show that the proposed decision-making method based on MILP-FRST model can accurately determine the quality level of the whole machine according to the assembly clearance parameters.

scholarly journals Three-Way Decisions Making Using Covering Based Fractional Orthotriple Fuzzy Rough Set Model

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1121 ◽  
Author(s):  
Shougi S. Abosuliman ◽  
Saleem Abdullah ◽  
Muhammad Qiyas
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Rough Sets ◽  
Fuzzy Numbers ◽  
Loss Functions ◽  
Multi Criteria Decision Making ◽  
Expected Loss ◽  
Fuzzy Rough Set ◽  
Decision Method ◽  
Proposed Model

On the basis of decision-theoretical rough sets (DTRSs), the three-way decisions give new model of decision approach for deal with the problem of decision. This proposed model of decision method is based on the loss function of DTRSs. First, the concept of fractional orthotriple fuzzy β -covering (FOF β -covering) and fractional orthotriple fuzzy β -neighborhood (FOF β -neighborhood) was introduced. We combined loss feature of DTRSs with covering-based fractional orthotriple fuzzy rough sets (CFOFSs) under the fractional orthotriple fuzzy condition. Secondly, we proposed a new FOF-covering decision-theoretical rough sets model (FOFCDTRSs) and developed related properties. Then, based on the grade of positive, neutral and negative membership of fractional orthotriple fuzzy numbers (FOFNs), five methods are established for addressing the expected loss expressed in the form of FOFNs and the corresponding three-way decisions are also derived. Based on this, we presented a FOFCDTRS-based algorithm for multi-criteria decision making (MCDM). Then, an example verifies the feasibility of the five methods for solving the MCDM problem. Finally, by comparing the results of the decisions of five methods with different loss functions.

scholarly journals Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

Computers ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 21
Author(s):  
Mehwish Leghari ◽  
Shahzad Memon ◽  
Lachhman Das Dhomeja ◽  
Akhtar Hussain Jalbani ◽  
Asghar Ali Chandio
Keyword(s):  
Feature Fusion ◽  
Real Life ◽  
Multimodal Biometrics ◽  
Proposed Model ◽  
Biometric Systems ◽  
Feature Level Fusion ◽  
Online Signature ◽  
Fusion Scheme ◽  
Fully Connected ◽  
Level Fusion

The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme.

A Framework for Various Attack Identification in MANET Using Multi-Granular Rough Set

2019 ◽  
Vol 13 (4) ◽  
pp. 28-52 ◽  
Author(s):  
N. Syed Siraj Ahmed ◽  
Debi Prasanna Acharjya
Keyword(s):  
Intrusion Detection ◽  
Rough Set ◽  
Equivalence Relations ◽  
Detection Model ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Proposed Model ◽  
Topology Changes ◽  
The Universe

The topology changes randomly and dynamically in a mobile adhoc network (MANET). The composite characteristics of MANETs makes it exposed to interior and exterior attacks. Avoidance support techniques like authentication and encryption are appropriate to prevent attacks in MANETs. Thus, an authoritative intrusion detection model is required to prevent from attacks. These attacks can be at either the layers present in the network or can be of a general attack. Many models have been developed for the detection of intrusion and detection. These models aim at any one of the layer present in the network. Therefore, effort has been made to consider either the layers for the detection of intrusion and detection. This article uses a multigranular rough set (MGRS) for the detection of intrusion and detection in MANET. The advantage of MGRS is that it can aim at either the layers present in the network simultaneously by using multiple equivalence relations on the universe. The proposed model is compared with many traditional models and attained higher accuracy.

scholarly journals MMPC-RF: A Deep Multimodal Feature-Level Fusion Architecture for Hybrid Spam E-mail Detection

2021 ◽  
Vol 11 (24) ◽  
pp. 11968
Author(s):  
Ghizlane Hnini ◽  
Jamal Riffi ◽  
Mohamed Adnane Mahraz ◽  
Ali Yahyaouy ◽  
Hamid Tairi
Keyword(s):  
Electronic Mail ◽  
Feature Space ◽  
Classification Model ◽  
Final Decision ◽  
Proposed Model ◽  
Feature Level Fusion ◽  
Strong Representation ◽  
Intelligent Approach ◽  
E Mail ◽  
Level Fusion

Hybrid spam is an undesirable e-mail (electronic mail) that contains both image and text parts. It is more harmful and complex as compared to image-based and text-based spam e-mail. Thus, an efficient and intelligent approach is required to distinguish between spam and ham. To our knowledge, a small number of studies have been aimed at detecting hybrid spam e-mails. Most of these multimodal architectures adopted the decision-level fusion method, whereby the classification scores of each modality were concatenated and fed to another classification model to make a final decision. Unfortunately, this method not only demands many learning steps, but it also loses correlation in mixed feature space. In this paper, we propose a deep multimodal feature-level fusion architecture that concatenates two embedding vectors to have a strong representation of e-mails and increase the performance of the classification. The paragraph vector distributed bag of words (PV-DBOW) and the convolutional neural network (CNN) were used as feature extraction techniques for text and image parts, respectively, of the same e-mail. The extracted feature vectors were concatenated and fed to the random forest (RF) model to classify a hybrid e-mail as either spam or ham. The experiments were conducted on three hybrid datasets made using three publicly available corpora: Enron, Dredze, and TREC 2007. According to the obtained results, the proposed model provides a higher accuracy of 99.16% compared to recent state-of-the-art methods.

Structure and Interaction of Protamine by Dark Field Electron Microscopy

1976 ◽  
Vol 34 ◽  
pp. 160-161
Author(s):  
D.P. Bazett-Jones ◽  
F.P. Ottensmeyer
Keyword(s):  
Electron Microscopy ◽  
Dark Field ◽  
Amino Acid Sequences ◽  
Dimensional Structure ◽  
3 Dimensional ◽  
Field Electron ◽  
Proposed Model ◽  
Field Electron Microscopy ◽  
Dark Field Electron ◽  
Positively Charged

It has been shown for some time that it is possible to obtain images of small unstained proteins, with a resolution of approximately 5Å using dark field electron microscopy (1,2). Applying this technique, we have observed a uniformity in size and shape of the 2-dimensional images of pure specimens of fish protamines (salmon, herring (clupeine, Y-l) and rainbow trout (Salmo irideus)). On the basis of these images, a model for the 3-dimensional structure of the fish protamines has been proposed (2).The known amino acid sequences of fish protamines show stretches of positively charged arginines, separated by regions of neutral amino acids (3). The proposed model for protamine structure (2) consists of an irregular, right-handed helix with the segments of adjacent arginines forming the loops of the coil.

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