MRI AND CT IMAGE FUSION BASED STRUCTURE-PRESERVING FILTER

Author :  Qiaoqiao Li

Affiliation :  Akita Prefectural University

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

Medical image fusion plays an important role in clinical application such as image-guided radiotherapy and surgery, and treatment planning. The main purpose of the medical image fusion is to fuse different multi-modal images, such as MRI and CT, into a single image. In this paper, a novel fusion method is proposed based on a fast structure-preserving filter for medical image MRI and CT of a brain. The fast structure preserving filter is a novel double weighted average image filter (SGF) which enables to smooth out high-contrast detail and textures while preserving major image structures very well. The workflow of the proposed method is as follows: first, the detail layers of two source images are obtained by using the structurepreserving filter. Second, compute the weights of each source image by calculating from the detail layer with the help of image statistics. Finally, fuse source images by weighted average using the computed weights. Experimental results show that the proposed method is superior to the existing medical image fusion method in terms of subjective evaluation and objective evaluation.

Keyword :  Multimodal image fusion, structure-preserving filter, weighted average.

For More Details :  https://airccj.org/CSCP/vol8/csit89712.pdf

REAL-TIME P2P STREAMING BASED ON PLAYBACK-RATE IN MANETS

Author :  Chia-Cheng Hu

Affiliation :  Yango University, Fuzhou

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

In a QoS-intensive multimedia application, Media-on-Demand (MoD) streaming can be delivered to asynchronous users with asynchronous requirement of MoD and VCR-like operation support. It is a critical challenge to propose a segment scheduling algorithm for real-time Peer to Peer (P2P) streaming services in mobile ad hoc networks (MANETs).However, it is a big challenge to provide MOD multimedia streaming to a large population of clients due to the asynchronous users. In this paper, we propose areal-time P2P scheduling algorithm by scheduling the segments evenly transmitted into the network according to the playback-rate of the real-time streaming service. The proposed algorithm schedules the segments from the peer with less bandwidth consumption to the network for further saving the limited bandwidth. On the other hand, it is adaptive to host mobility. Extensive simulations illustrate the effectiveness of the proposed scheme.

Keyword :  Mobile Ad Hoc Networks, Playback Rate, Segment Scheduling and Timely P2P Streaming

For More Details :  https://airccj.org/CSCP/vol8/csit89711.pdf

SAITE STORE 2.0: EXPERIENCE REPORT ON THE DEVELOPMENT OF AN IMPROVED VERSION OF A DIGITAL LIBRARY APPLICATION

Author :  Ana Emilia Figueiredo de Oliveira

Affiliation :  Department of Dentistry I, Federal University of Maranhão, São Luís

Country :  Brazil

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

The use of mobile technologies in the educational process has been generating positive results. In this context, institutions that work with Distance Education (DE) need to update and adapt their processes to these innovations. Considering this trend, UNA-SUS/UFMA has built, in partnership with the Saite Group, a virtual library that enables fast and free access to its contents using mobile devices. With the expansion of the tool's use, the institution invested in a new version to provide a better experience to its users. This paper aims to perform a detailing of the Saite Store new version, showing its operation and the technological implementations carried out. Finally, the importance of performing updates in the application is highlighted, considering its great potential for use by more than twenty thousand users.

Keyword :  Distance education, virtual library, e-learning, mobile.

For More Details :  https://airccj.org/CSCP/vol8/csit89710.pdf

ANDROID MALWARE DETECTION USING MACHINE LEARNING AND REVERSE ENGINEERING

 Author :  Michal Kedziora

Affiliation :  Wroclaw University of Science and Technology Wroclaw

Country :  Poland

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

This paper is focused on the issue of malware detection for Android mobile system by Reverse Engineering of java code. The characteristics of malicious software were identified based on a collected set of applications. Total number of 1958 applications where tested (including 996 malware apps). A unique set of features was chosen. Five classification algorithms (Random Forest, SVM, K-NN, Nave Bayes, Logistic Regression) and three attribute selection algorithms were examined in order to choose those that would provide the most effective malware detection.

Keyword :  Malware Detection, Android, Random Forest, SVM, K-NN, Naive Bayes, Logistic Regression 

For More Details :  https://airccj.org/CSCP/vol8/csit89709.pdf

CYBER-ATTACKS ON THE DATA COMMUNICATION OF DRONES MONITORING CRITICAL INFRASTRUCTURE

 Author :  Hadjer Benkraouda

Affiliation :  United Arab Emirates University

Country :  AE

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

With the exponential growth in the digitalization of critical infrastructures such as nuclear plants and transmission and distribution grids, these systems have become more prone to coordinated cyber-physical attacks. One of the ways used to harden the security of these infrastructures is by utilizing UAVs for monitoring, surveillance and data collection. UAVs use data communication links to send the data collected to ground control stations (GCSs). The literature [1] suggests that there is a lack of research in the area of the cybersecurity of data communication from drones to GCSs. Therefore, this paper addresses this research gap and analyzes the vulnerabilities and attacks on the collected sensor data, mainly on: data availability, data integrity and data confidentiality, and will propose solutions for securing the drone’s data communication systems.

Keyword :  Information security, UAV Security, Critical Infrastructure Security.

For More Details :  https://airccj.org/CSCP/vol8/csit89708.pdf

RESIDENTIAL LOAD PROFILE ANALYSIS USING CLUSTERING STABILITY

 Author :  Fang-Yi Chang

Affiliation :  Digital Transformation Institute, Institute for Information Industry

Country :  Taiwan

Category :  Soft Computing

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

Clustering is an useful tool in the data analysis to discover the natural structure in the data. The technique separates given smart meter data set into several representative clusters for the convenience of energy management. Each cluster may has its own attributes, such as energy usage time and magnitude. These attributes can help the electrical operators to manage their electrical grids with goals of energy and cost reduction. In this paper, we use principle component analysis and K-means as dimensional reduction and the reference clustering algorithm, respectively, and several choices must be considered: the number of cluster, the number of the leading principle components, and whether use normalized principle analysis schema or not. To answer these issues simultaneously, we use the stability scores as measured by dot similarity and confusion matrix as our evaluation decision. The advantage is that it is useful for comparing the performance under different decisions, and thus provides us to make these choices simultaneously.

Keyword :  Smart meter; Unsupervised; Nonparameter; Clustering; PCA; Stability; Smart Grid; Value-Add Electricity Services; Energy Saving; Energy management

For More Details :  https://airccj.org/CSCP/vol8/csit89707.pdf

ADAPTABASE - ADAPTIVE MACHINE LEARNING BASED DATABASE CROSSTECHNOLOGY SELECTION

 Author :  Shay Horovitz

Affiliation :  School of Computer Science, College of Management Academic Studies

Country :  Israel

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

As modern applications and systems are growing fast and continuously changing, back-end services in general and database services in particular are being challenged with dynamic loads and differential query behaviour. The traditional best practice of designing database – creating fixed relational schemas prior to deployment - becomes irrelevant. While newer database technologies such as document based and columnar are more flexible, they perform better only under certain conditions that are hard to predict. Frequent manual modifications of database structures and technologies under production require expert skills, increase management costs and often ends up with sub-optimal performance. In this paper we propose AdaptaBase - a solution for performance optimization of database technologies in accordance with application query demands by using machine learning to model application query behavioural patterns and learning the optimal database technology per each behavioural pattern. Experiments present a reduction in query execution time of over 25% for the relational-columnar model selection, and over 30% for the relation-document based model selection.

Keyword :  Database, Cross-Technology, Machine Learning, Adaptive

For More Details :  https://airccj.org/CSCP/vol8/csit89706.pdf

PHISHING DETECTION FROM URLS BY USING NEURAL NETWORKS

 Author :  Ozgur Koray Sahingoz

Affiliation :  Istanbul Kultur University

Country :  Turkey

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

In recent years, Internet technologies are grown pervasively not only in information-based web pages but also in online social networking and online banking, which made people’s lives easier. As a result of this growth, computer networks encounter with lots of different security threats from all over the world. One of these serious threats is “phishing”, which aims to deceive their victims for getting their private information such as username, passwords, social security numbers, financial information, and credit card number by using fake e-mails, webpage’s or both. Detection of phishing attack is a challenging problem, because it is considered as a semantics-based attack, which focuses on users’ vulnerabilities, not networks’ vulnerabilities. Most of the anti-phishing tools mainly use the blacklist/white list methods; however, they fail to catch new phishing attacks and results a high false-positive rate. To overcome this deficiency, we aimed to use a machine learning based algorithms, Artificial Neural Networks(ANNs) and Deep Neural Networks(DNNs), for training the system and catch abnormal request by analysing the URL of web pages. We used a dataset which contains 37,175 phishing and 36,400 legitimate web pages to train the system. According to the experimental results, the proposed approaches has the accuracy in detection of phishing websites with the rate of 92 % and 96 % by the use of ANN and DNN approaches respectively.

Keyword :  Phishing Detection System, Artificial Neural Networks, Deep Neural Networks, Big Data, Machine Learning, Tensor flow, Feature Extraction

For More Details :  https://airccj.org/CSCP/vol8/csit89705.pdf

POSSIBILITIES OF PYTHON BASED EMOTION RECOGNITION

Author :  Primož Podržaj

Affiliation :  University of Ljubljana

Country :  Slovenia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, February, 2018

Abstract :

Vision is probably the most important sense for human beings. As a consequence, our way of behaviour and thinking is also often based on visual information. When trying to perform complex information especially in situations where humans are involved, it is of great benefit if some information can be obtained from images. This is the field of image processing and computer vision. There are various libraries available for these tasks. Probably the best known one is OpenCV. It can also be used in Python programming language. Simple and more complex image processing algorithms are already available in the library. One of the more complex ones is face detection. In this paper it shown how face detection can be executed within Python with OpenCV library. This is the first step needed in emotion recognition. When face is detected, we can determine the emotional state of the subject using a special purpose library.

Keyword :  Image processing, Python, OpenCV, face detection, emotion recognition.

For More Detail :  https://airccj.org/CSCP/vol8/csit89704.pdf

A POST-PROCESSING METHOD BASED ON FULLY CONNECTED CRFS FOR CHRONIC WOUND IMAGES SEGMENTATION AND IDENTIFICATION

 Author :  Junnan Zhang

Affiliation :  Computer College, NUDT, Changsha

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, February, 2018

Abstract :

Chronic wound have a long recovery time, occur extensively, and are difficult to treat. They cause not only great suffering to many patients but also bring enormous work burden to hospitals and doctors. Therefore, an automated chronic wound detection method can efficiently assist doctors in diagnosis, or help patients with initial diagnosis, reduce the workload of doctors and the treatment costs of patients. In recent years, due to the rise of big data, machine learning methods have been applied to Image Identification, and the accuracy of the result has surpassed that of traditional methods. With the fully convolutional neural network proposed, image segmentation and target detection have also achieved excellent results. However, the accuracy of chronic wound image segmentation and identification is low due to the limitation of the deep convolution neural network. To solve the above problem, we propose a post-processing method based on fully connected CRFs with multi-layer score maps. The experiment results show that our method can be used to improve the accuracy of chronic wound image segmentation and identification.

Keyword :  Fully Connected CRFs, Chronic Wound Segmentation, Post-processing Method

For More Details :  https://airccj.org/CSCP/vol8/csit89703.pdf

A PSEUDO-SPLICING ALGORITHM FOR PARTIAL FINGERPRINT RECOGNITION BASED ON SIFT

 Author :  Zheng Zhu, Aiping Li

Affiliation :  National University of Defense Technology

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, February, 2018

Abstract :

At present, many fingerprint recognition techniques are applied to public infrastructures. Their targets are mainly for normal-sized fingerprints. However, with the rise of small-sized fingerprint sensors, the acquired partial fingerprints containing only part of information of the finger, which causes that many researchers change their research directions to partial fingerprint recognition. This paper proposes a SIFT-based pseudo-splicing partial fingerprint recognition algorithm. This algorithm uses the SIFT algorithm to pseudo-splice the input fingerprints during the fingerprint enrollment to increase the robustness of the fingerprint feature database. The comparisons of the accuracy of the recognition among this algorithm, the minutia-based fingerprint recognition algorithm and the fingerprint recognition algorithm based on image similarity, that shows the first performs well. Moreover, this paper proposes an algorithm to evaluate the quality of partial finger print by calculating the invalid blocks of fingerprint image. The result shows that the evaluation algorithm can effectively filter out low-quality fingerprints.

Keyword :  Fingerprint Recognition, SIFT, Partial Fingerprint, Pseudo-splicing

For More Details :  https://airccj.org/CSCP/vol8/csit89702.pdf

TOMOGRAPHIC SAR INVERSION FOR URBAN RECONSTRUCTION

 Author :  Karima Hadj-Rabah

Affiliation :  University of Sciences and Technology Houari Boumediene (USTHB)

Country :  Algeria

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 17, December, 2018

Abstract :

Given its efficiency and its robustness in separating the different scatterers present in the same resolution cell, SAR tomography (TomoSAR) has become an important tool for the reflectivity reconstruction of the observed complex structures scenes by exploiting multi-dimensional data. By its principle, TomoSAR reduces geometric distortions especially the layover phenomenon in radar scenes, and thus reconstruct the 3D profile of each azimuth-range pixel. In this paper, we present the results and the comparative study of six tomographic reconstruction methods that we have implemented. The analysis is performed with respect to the separability and location of scatterers by each method, supplemented by the proposal of a quantitative analysis using metrics (accuracy and completeness) to evaluate the robustness of each method. The tests were applied on simulated data with TerraSAR-X sensor parameters.

Keyword :  SAR Tomography (TomoSAR), Reconstruction Algorithms, Accuracy& Completeness

For More Details :  https://airccj.org/CSCP/vol8/csit89701.pdf

SECURITY PROTOCOL FOR POLLUTION ATTACK USING NETWORK CODING

 Author :  Kiattikul Sooksomsatarn

Affiliation :  University of Phayao

Country :  Thailand

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

Network coding is a technique for maximizing the use of available bandwidth capacity. We are interested in applying network coding to multimedia content distribution. This is desirable because many popular network applications for content distribution consume high bandwidth and international bandwidth; both are scarce in countries such as New Zealand. Existing work has addressed the use of network coding for content distribution, however work on network coding and security does not consider the trade-off between quality of service and security for multimedia. Network coding is vulnerable to a pollution attack or a packet modification attack. It has detrimental effect particularly on network coding because of specific characteristic of network coding that allows nodes to modify received packets at any time. Many pollution attack defence mechanisms use computationally expensive techniques leading to higher communication cost. Therefore, the focus of this work is on developing protocols to address both open problems and validate the protocols using a combination of formal and simulation techniques. More importantly, our novel contribution is reduction of complexity of algorithms appropriate for streaming content distribution with network coding.

Keyword :  Network Coding, Pollution Attack Detection, Security Protocol

For More Details :  https://airccj.org/CSCP/vol8/csit89816.pdf

BLACK HOLE ATTACK SECURITY ISSUES, CHALLENGES & SOLUTION IN MANET

 Author :  Muneer Bani Yassein

Affiliation :  Faculty of Computer & Information Technology

Country :  Jordan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

MANET (Mobile Ad-hoc Network) is simply a set of mobile hosts connected wirelessly without any centralized management, where each node acts as a packet sender, packet receiver, and a router at the same time. According to the nature of this network, the dynamic topology and the absence of a centralized management cause several security issues and attacks, such as the black hole attack, the wormhole attack, and the impersonation and repudiation attack. In this survey, we are going to introduce the Black Hole attack security issues and some of the detection techniques used to detect the black hole attack. In this kind of attack (black hole attack) the intruders manipulate the normal behavior of the network, by introducing themselves as the node with the shortest path to the destination. Intruders can do a malicious behavior over the network.

Keyword :  MANET, Routing Protocols, Black Hole Attack, AODV, DSR, RREQ, RREP, RERR

For More Details :  https://airccj.org/CSCP/vol8/csit89815.pdf

Call for Papers - International Conference on NLP & Artificial Intelligence Techniques (NLAI 2020)

 International Conference on NLP & Artificial Intelligence
Techniques (NLAI 2020)

December 19 ~ 20, 2020, Sydney, Australia

https://nlai2020.org/

Important Dates

Submission Deadline	:	November 14, 2020
Authors Notification	:	November 30, 2020
Final Manuscript Due	:	December 08, 2020

Contact Us : nlai@nlai2020.org

More Details : https://allconferencecfpalerts.com/cfp/view.php?eno=22552

RANDOMIZED DYNAMIC TRICKLE TIMER ALGORITHM FOR INTERNET OF THINGS

 Author :  Muneer Bani Yassein

Affiliation :  Jordan University of Science and Technology

Country :  Jordan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

Routing Protocol for Low Power and Lossy Networks (RPL) is one of the most utilized routing protocols. It designed to adapt with thousands of nodes in energy-constrained networks. It is a proactive distance vector protocol which has two major components objective function and trickle algorithm. Our work focus on the trickle timer algorithm, it is used to control, maintain and follow the control messages over the network. Short listen problem is the main blot in trickle algorithm. Several studies focused on enlarging the listen period. However, as it was suffering from node starvation when the period is short, it suffers from time and energy wasting when the period is enlarged. Notice that the time and power consumption are sensitive factors in Low Power and Lossy Networks. In this paper, we propose a randomized dynamic trickle algorithm, it contributes in the improvement of trickle and solving the above-mentioned problems by controlling the t variable in a dynamic randomly way, where t is the border line between listening and transmitting period. The performance of the proposed algorithm is validated through extensive simulation experiments under different scenarios and operation conditions using Cooja 2.7 simulator. Simulation results compared with the standard trickle timer algorithm based on convergence time, packet delivery ratio (PDR) and power consumption performance metrics. The results of the simulations denote a high improvement in term of convergence time, power consumption and packet delivery ratio

Keyword :  Routing Protocol for Low Power and Lossy Networks(RPL), Internet of Things (IoT), Trickle Timer Algorithm.

For More Details :  https://airccj.org/CSCP/vol8/csit89814.pdf

A PREFERMENT PLATFORM FOR IMPLEMENTING SECURITY MECHANISM FOR AUTOMOTIVE CAN BUS

 Author :  Mabrouka Gmiden

Affiliation :  Computer and Embedded System Lab (CES)

Country :  Tunisia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

The design of cryptographic mechanisms in automotive systems has been a major focus over the last ten years as the increase of cyber attacks against in-vehicle networks. The integration of these protocols into CAN bus networks is an efficient solution for leaving security level, but features of CAN bus make the performance requirements within cryptographic schemes very challenging. In the literature most of academic researches focused on designing security mechanisms for the CAN bus. Yet, very few research proposals are interested in analyzing performances requirements by using cryptographic protocols. In this paper, we investigate effects of implementing cryptographic approaches on performance by proposing an analysis methodology for implementing cryptographic approach in CAN bus communication and measuring real-time performances. Next, we propose our system which presents a tool for determining the impact of implementing of cryptographic solutions. On the other hand we have proposed an intrusion detection system using the same platform. Our tool allows the implementation of any security strategy as well as the real-time performance analysis of CAN network.

Keyword :  CAN bus, In-vehicle Network, Security, Analysing

For More Details :  https://airccj.org/CSCP/vol8/csit89813.pdf

Call for Papers - 4th International Conference on Artificial Intelligence, Soft Computing And Applications (AISCA 2020)

 4^th International Conference on Artificial Intelligence, Soft Computing And Applications (AISCA 2020)

https://aisca2020.org/index.html

November 28~29, 2020, Dubai, UAE

Important Dates

Submission Deadline : November 08, 2020

Authors Notification : November 15, 2020

Registration & Camera-Ready Paper Due : November 18, 2020

Contact Us :  aisca@aisca2020.org

More Details : https://allconferencecfpalerts.com/cfp/view.php?eno=22487

NEAR-DROWNING EARLY PREDICTION TECHNIQUE USING NOVEL EQUATIONS (NEPTUNE) FOR SWIMMING POOLS

 Author :  B David Prakash

Affiliation :  IAG Firemark

Country :  Singapore

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

Safety is a critical aspect in all swimming pools. This paper describes a near-drowning early prediction technique using novel equations (NEPTUNE). NEPTUNE uses equations or rules that would be able to detect near-drowning using at least 1 but not more than 5 seconds of video sequence with no false positives. The backbone of NEPTUNE encompasses a mix of statistical image processing to merge images for a video sequence followed by K-means clustering to extract segments in the merged image and finally a revisit to statistical image processing to derive variables for every segment. These variables would be used by the equations to identify near-drowning. NEPTUNE has the potential to be integrated into a swimming pool camera system that would send an alarm to the lifeguards for early response so that the likelihood of recovery is high.

Keyword :  Near-drowning Detection, Drowning Detection, Statistical Image Processing, K-means Clustering, Swimming Pools

For More Details :  https://airccj.org/CSCP/vol8/csit89812.pdf

VIDEO SEQUENCING BASED FACIAL EXPRESSION DETECTION WITH 3D LOCAL BINARY PATTERN VARIANTS

 Author :  Kennedy Chengeta

Affiliation :  University of KwaZulu Natal

Country :  South Africa

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

Facial expression recognition in the field of computer vision and texture synthesis is in two forms namely static image analysis and dynamic video textures. The former involves 2D image texture synthesis and the latter dynamic textures where video sequences are extended into the temporal domain taking into account motion. The spatial domain texture involves image textures comparable to the actual texture and the dynamic texture synthesis involves videos which are given dynamic textures extended in a spatial or temporal domain. Facial actions cause local appearance changes over time, and thus dynamic texture descriptors should inherently be more suitable for facial action detection than their static variants. A video sequence is defined as a spatial temporal collection of texture in the temporal domain where dynamic features are extracted. The paper uses LBP-TOP which is a Local Binary Pattern variant to extract facial expression features from a sequence of video datasets. Gabor Filters are also applied to the feature extraction method. Volume Local Binary Patterns are then used to combine the texture, motion and appearance. A tracker was used to locate the facial image as a point in the deformation space. VLBP and LBP-TOP clearly outperformed the earlier approaches due to inclusion of local processing, robustness to monotonic gray-scale changes, and simple computation. The study used Facial Expressions and Emotions Database(FEED) and CK+ databases. The study for the LBP -TOP and LGBP-TOP achieved bettered percentage recognition rate compared to the static image local binary pattern with a set of 333 sequences from the Cohn–Kanade database.

Keyword :  Local binary patterns on Three Orthogonal Planes (LBPTOP) · Volume Local Binary Patterns(VLBP)

For More Details :  https://airccj.org/CSCP/vol8/csit89811.pdf

A MACHINE LEARNING APPROACH TO DETECT AND CLASSIFY 3D TWO-PHOTON POLYMERIZATION MICROSTRUCTURES USING OPTICAL MICROSCOPY IMAGES

 Author :  Israel Goytom

Affiliation :  Ningbo University

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

For 3D microstructures fabricated by two-photon polymerization, a practical approach of machine learning for detection and classification in their optical microscopic images is state and demonstrated in this paper. It is based on Faster R-CNN, Multi-label classification (MLC) and Residual learning framework Algorithms for reliable, automated detection and accurate labeling of Two Photo Polymerization (TPP) microstructures. From finding and detecting the microstructures from a different location in the microscope slide, matching different shapes of the microstructures classify them among their categories is fully automated. The results are compared with manual examination and SEM images of the microstructures for the accuracy test. Some modifications of ordinary optical Microscope so as to make it automated and by applying Deep learning and Image processing algorithms we can successfully detect, label and classify 3D microstructures, designing the neural network model for each phase and by training them using the datasets we have made, the dataset is a set of different images from different angles and their annotation we can achieve high accuracy. The accurate microstructure detection technique in the combination of image processing and computer vision help to simulate the values of each pixel and classify the Microstructures.

Keyword :  Multi-label classification, Faster R-CNN Two-Photon Polymerization, computer vision, 3D Microstructures

For More Details :  https://airccj.org/CSCP/vol8/csit89810.pdf

AN ELASTIC-HYBRID HONEYNET FOR CLOUD ENVIRONMENT

 Author :  Nguyen Khac Bao

Affiliation :  Soongsil University

Country :  Korea

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 18, February, 2018

Abstract :

When low-interaction honey net systems are not powerful enough and high-interaction honey net systems require a lot of resources, hybrid solutions offer the benefit’s of both worlds. Affected by this trend, more and more hybrid honey net systems have been proposed to obtain wide coverage of attack traffic and high behavioral ideality in recent years. However, these system themselves contain some limitations such as the high latency, the lack of prevention method for compromised honey pots, the waste of resources and the finger printing problem of honey pot that hinder them to achieve their goals. To address these limitations, we propose a new honey net architecture called Efficient Elastic Hybrid Honey net. Utilizing the advantages of combining SDN and NFV technologies, this system can reduce the response time for attack traffic, isolate compromised honey pots effectively, defeat the finger printing problem of honey pots, and optimize the resources for maintenance and deployment. Testing our system with real attack traffic, the results have showed that Efficient Elastic-Hybrid Honey net system is not only practical, but also very efficient.

Keyword :  Honey net, Honey pot, Elastic, Hybrid, Software defined Networking, Network Function Virtualization

For More Details :  https://airccj.org/CSCP/vol8/csit89809.pdf