CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCH

Author :  Shannon Heh

Affiliation :  Lynbrook High School San Jose, California

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 6, April, 2018

Abstract :

Data collection is an essential, but manpower intensive procedure in ecological research. An algorithm was developed by the author which incorporated two important computer vision techniques to automate data cataloging for butterfly measurements. Optical Character Recognition is used for character recognition and Contour Detection is used for imageprocessing. Proper pre-processing is first done on the images to improve accuracy. Although there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify words of basic fonts. Contour detection is an advanced technique that can be utilized to measure an image. Shapes and mathematical calculations are crucial in determining the precise location of the points on which to draw the body and forewing lines of the butterfly. Overall, 92% accuracy were achieved by the program for the set of butterflies measured.

Keyword :  Computer Vision, Image Recognition, Character Recognition, Ecology, Butterfly Cataloging

For More Details :  https://airccj.org/CSCP/vol8/csit88606.pdf

SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CITIES

 Author :  Aysha Al Nuaimi

Affiliation :  United Arab Emirates University

Country :  United Arab Emirates

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 6, April, 2018

Abstract :

Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city services including energy, transportation, health, and much more. They generate massive volumes of structured and unstructured data on a daily basis. Also, social networks, such as Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart cities. Social network users are acting as social sensors. These datasets so large and complex are difficult to manage with conventional data management tools and methods. To become valuable, this massive amount of data, known as 'big data,' needs to be processed and comprehended to hold the promise of supporting a broad range of urban and smart cities functions, including among others transportation, water, and energy consumption, pollution surveillance, and smart city governance. In this work, we investigate how social media analytics help to analyze smart city data collected from various social media sources, such as Twitter and Facebook, to detect various events taking place in a smart city and identify the importance of events and concerns of citizens regarding some events. A case scenario analyses the opinions of users concerning the traffic in three largest cities in the UAE

Keyword :  Amharic Hate speech detection, Social networks and spark, Amharic posts and comments

For More Details :  https://airccj.org/CSCP/vol8/csit88605.pdf

SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGE

 Author :  Zewdie Mossie

Affiliation :  National Taipei University of Technology

Country :  Taiwan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 6, April, 2018

Abstract :

The anonymity of social networks makes it attractive for hate speech to mask their criminal activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing volume of social media data, hate speech identification becomes a challenge in aggravating conflict between citizens of nations. The high rate of production, has become difficult to collect, store and analyze such big data using traditional detection methods. This paper proposed the application of apache spark in hate speech detection to reduce the challenges. Authors developed an apache spark based model to classify Amharic Facebook posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation, the model based on word2vec embedding performed best with 79.83%accuracy. The proposed method achieve a promising result with unique feature of spark for big data.

Keyword :  Amharic Hate speech detection, Social networks and spark, Amharic posts and comments

For More Details :  https://airccj.org/CSCP/vol8/csit88604.pdf

GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXT

 Author :  ArchitYajnik

Affiliation :  Sikkim Manipal University

Country :  India

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 6, April, 2018

Abstract :

This article presents Part of Speech tagging for Nepali text using General Regression Neural Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is trained and validated on both training and testing data. It is observed that 96.13% words are correctly being tagged on training set whereas 74.38% words are tagged correctly on testing data set using GRNN. The result is compared with the traditional Viterbi algorithm based on Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on training and testing data sets respectively. GRNN based POS Tagger is more consistent than the traditional Viterbi decoding technique.

Keyword :  General Regression Neural Networks, Viterbi algorithm, POS tagging

For More Details :  https://airccj.org/CSCP/vol8/csit88603.pdf

APPLYING DISTRIBUTIONAL SEMANTICS TO ENHANCE CLASSIFYING EMOTIONS IN ARABIC TWEETS

 Author :  Shahd Alharbi

Affiliation :  King Saud University

Country :  UK

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 6, April, 2018

Abstract :

Most of the recent researches have been carried out to analyse sentiment and emotions found in English texts, where few studies have been conducted on Arabic contents, which have been focused on analysing the sentiment as positive and negative, instead of the different emotions’ classes. Therefore this paper has focused on analysing different six emotions’ classes in Arabic contents, especially Arabic tweets which have unstructured nature that make it challenging task compared to the formal structured contents found in Arabic journals and books. On the other hand, the recent developments in the distributional sematic models, have encouraged testing the effect of the distributional measures on the classification process, which was not investigated by any other classification-related studies for analysing Arabic texts. As a result, the model has successfully improved the average accuracy to more than 86% using Support Vector Machine (SVM) compared to the different sentiments and emotions studies for classifying Arabic texts through the developed semi-supervised approach which has employed the contextual and the co-occurrence information from a large amount of unlabelled dataset. In addition to the different remarkable achieved results, the model has recorded a high average accuracy, 85.30%, after removing the labels from the unlabelled contextual information which was used in the labelled dataset during the classification process. Moreover, due to the unstructured nature of Twitter contents, a general set of pre-processing techniques for Arabic texts was found which has resulted in increasing the accuracy of the six emotions’ classes to 85.95% while employing the contextual information from the unlabelled dataset.

Keyword :  SVM, DSM, classifying, Arabic tweets, hashtags, emoticons, NLP &co-occurrence matrix.

For More Details :  https://airccj.org/CSCP/vol8/csit88602.pdf

NEURAL SYMBOLIC ARABIC PARAPHRASING WITH AUTOMATIC EVALUATION

 Author :  Fatima Al-Raisi

Affiliation :  Carnegie Mellon University

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 06, April, 2018

Abstract :

We present symbolic and neural approaches for Arabic paraphrasing that yield high paraphrasing accuracy. This is the first work on sentence level paraphrase generation for Arabic and the first using neural models to generate paraphrased sentences for Arabic. We present and compare several methods for para- phrasing and obtaining monolingual parallel data. We share a large coverage phrase dictionary for Arabic and contribute a large parallel monolingual corpus that can be used in developing new seq-to-seq models for paraphrasing. This is the first large monolingual corpus of Arabic. We also present first results in Arabic paraphrasing using seq-to-seq neural methods. Additionally, we propose a novel automatic evaluation metric for paraphrasing that correlates highly with human judgement.

Keyword :  Natural Language Processing, Paraphrasing, Sequence-to-Sequence Models, Neural Networks, Automatic Evaluation, Evaluation Metric, Data Resource

For More Details :  https://airccj.org/CSCP/vol8/csit88601.pdf

COMPARATIVE STUDY BETWEENDECISION TREES AND NEURAL NETWORKS TO PREDICTFATAL ROAD ACCIDENTSIN LEBANON

 Author :  Zeinab FARHAT

Affiliation :  Lebanese University

Country :  France

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  9, 11, August, 2019

Abstract :

Nowadays, road traffic accidents are one of the leading causes of deaths in this world. It is a complex phenomenon leaving a significant negative impact on human’s life and properties. Classification techniques of data mining are found efficient to deal with such phenomena. After collecting data from Lebanese Internal Security Forces, data are split into training and testing sets using 10-fold cross validation. This paper aims to apply two different algorithms of Decision Trees C4.5 and CART, and various Artificial Neural Networks (MLP) in order to predict the fatality of road accidents in Lebanon. Afterwards, a comparative study is made to find the best performing algorithm. The results have shown that MLP with 2 hidden layers and 42 neurons in each layer is the best algorithm with accuracy rate of prediction (94.6%) and area under curve (AUC 95.71%).

Keyword :  Data mining, Fatal Road Accident Prediction, Neural Networks, Decision trees

For More Details :  https://aircconline.com/csit/papers/vol9/csit91101.pdf

ARCHITECTURE AND TECHNICAL DEBT AGILE PLANNING METHODOLOGY FOR SOFTWARE PRODUCTION

 Author :  Aya Elgebeely

Affiliation :  Cairo University

Country :  Egypt

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  7, 17, December, 2017

Abstract

This paper shows an empirical study for a new agile release planning methodology. The case study includes the application of the methodology by two teams in different software business domains (Game development and medical software development). The suggested methodology showed clear improvements in teams’ productivity, enhanced software quality and better handling of the overall software architecture and technical debt. It allowed software teams to have more predictable release plan with fewer technical uncertainties. Results are showed in comparison with the traditional scrum release planning approach.

Keyword :  Agile, Technical Debt, Release Planning, Software Architecture, Software Engineering

For More Details  :  https://airccj.org/CSCP/vol7/csit77903.pdf

MACHINE TRANSLATION EVALUATION IN SNS IN TERMS OF USER-CENTERED ORIENTATION

Author :  Kim Euna

Affiliation :  Department of English Language & Literature, Busan

Country :  South Korea

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  7, 17, December, 2017

Abstract :

This Study explores the role of machine translation by creating a corpus of text from the one of SNS, Facebook, and analyzing and evaluating the corpus data in terms of User-Centered Translation (UCT). For the data to examine, Reuter’s Facebook account with language pair of English and Korean was selected due to the fact that the posts are open to the public and use a formal structure of sentences. Based on the corpus, a questionnaire was made to actually see the response from users who are following the Reuter’s account and using translation function.

Keyword :  Machine Translation, Social Network Services, Corpus, User-Cantered Translation, Target Reader

For More Details :  https://airccj.org/CSCP/vol7/csit77902.pdf

STUDY ON DOCUMENTARY TRANSLATION FOR DUBBING

 Author :  Yunsil Jo

Affiliation :  Pusan National University

Country :  South Korea

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  7, 17, December, 2017

Abstract :

This paper discusses the features of documentary translation for dubbing and translation strategies for this kind of audiovisual genre. Especially it aims to analyze differences in the use of pronouns between source text and target text by making use of parallel corpus of English documentary scripts and their Korean translated versions. It is argued that these differences and translation strategies might be attributed to the viewers’ expectancy described in Chesterman’s norm theory.

Keyword :  Documentary translation, The Voice-over Translation of Documentaries, Audiovisual translation, Translation for dubbing, Chesterman’s norm theory.

For More Details :  https://airccj.org/CSCP/vol7/csit77901.pdf

A NETWORK OF INTELLIGENT PROXIMITY IOT DEVICES FOR OBJECT LOCALIZATION, INFORMATION COMMUNICATION, AND DATA ANALYTICS BASED ON CROWDSOURCING

 Author :  Mike Qu

Affiliation :  Northwood High School, Irvine, CA 92602

Country :  USA

Category :  Computer Science & Information Technology

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

Abstract :

While the advancements of technology have benefited the society in many ways, certain problems remain, and one such problem is the issue of lost people and pets. Current technology has offered many solutions to this problem, yet none is able to encompass all the core aspects to bring an end to the problem. My research proposes a solution that is practical, durable and reliable -- a proximity sensor device powered by a crowd of people, by using their mobile devices as receiving stations of the service, extensively increasing the effectiveness of this service in especially urban and suburban areas where there is a high population density.

Keyword :  Beacon, Device Network, Crowdsourcing, Double-blind, Artificial Intelligence.

For More Details :  https://airccj.org/CSCP/vol8/csit89716.pdf

SCALABLE DYNAMIC LOCALITYSENSITIVE HASHING FOR STRUCTURED DATASET ON MAIN MEMORY AND GPGPU MEMORY

 Author :  Toan Nguyen Mau

Affiliation :  Inoguchi Laboratory, School of Information Science, JAIST

Country :  Japan

Category :  Information Technology Management

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

Abstract :

Locality-sensitive hashing(LSH) is a significant algorithm for big-data hashing. The original LSH uses a static hash-table as a reduce mapping for the data. Which make LSH challenging to apply on real-time information retrieval system. The database of a realtime system needs to be scalably updated over time. In this research, we concentrate on increasing the accuracy, searching speed and throughput of the nearest neighbor searching problem on big dynamic database. The dynamic Locality-sensitive hashing(DLSH) is proposed for facing the static problem of original LSH. DLSH is targeted for deploying on main memory or GPGPU's global memory, which can increase the throughput searching by parallel processing on multiple cores. We analyzed the efficiency of DLSH by building the big dataset of structured audio fingerprint and comparing the performance with original LSH. To achieve the dynamics, DLSH requires more memory space and takes slightly slower than the LSH. With DLSH's advantages, it can be improved and fully applied in practice in a real-life information retrieval system.

Keyword :  Locality-sensitive hashing, Structured dataset, GPGPU Memory, Similarity Searching, Parallel Processing

Journal/ Proceedings Name :  Computer Science & Information Technology

For More Details :  https://airccj.org/CSCP/vol8/csit89715.pdf

ENHANCING COMPUTER NETWORK SECURITY ENVIRONMENT BY IMPLEMENTING THE SIX-WARE NETWORK SECURITY FRAMEWORK (SWNSF)

Author :  Rudy Agus Gemilang Gultom

Affiliation :  Indonesia Defense University

Country :  Indonesia

Category :  Computer Science & Information Technology

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

Abstract :

This paper proposes a network security framework concept, so called the Six-Ware Network Security Framework (SWNSF). The SWNSF aim is to increase a Local Area Network (LAN) security readiness or awareness in a network security environment. This SWNSF proposal is proposed in order to enhance an organization’s network security environment based on cyber protect simulation experiences. Strategic thoughts can be implemented during cyber protect simulation exercise. Brilliant ideas in simulating an network security network environment become good lesson learned. The implementation for proper security strategy could secure an organization LAN from various threats, attack and vulnerabilities in concrete and abstract levels. Countermeasure strategy, which is implemented in this simulation exercise is presented as well. At the end of this paper, an initial network security framework proposal, so called the Six-Ware Network Security Framework has been introduced.

Keyword :  Network security environment; cyber protect simulation; cyber threats, attack and vulnerabilities; countermeasures strategy, LAN, SWNSF framework

For More Details :  https://airccj.org/CSCP/vol8/csit89714.pdf

Call for Papers - 10th International Conference on Control, Modelling, Computing and Applications (CMCA 2021)

 10^th International Conference on Control, Modelling, Computing and Applications (CMCA 2021)

https://necom2021.org/cmca/index

February 20~21, 2021, Dubai, UAE

Important Dates

Submission Deadline	:	December 06, 2020
Authors Notification	:	January 10, 2021
Final Manuscript Due	:	January 18, 2021

Contact Us:  cmca@necom2021.org

More Details: https://allconferencecfpalerts.com/cfp/view.php?eno=23118

ANTI-VIRUS TOOLS ANALYSIS USING DEEP WEB MALWARES

Author :  Igor Mishkovski

Affiliation :  University Ss. Cyril and Methodius, FCSE, Skopje, 1000, Macedonia

Country :  Finland

Category :  Computer Science & Information Technology

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

Abstract :

Knowledge about the strength of the anti-virus engines (i.e. tools) to detect malware files on the Deep web is important for people and companies to devise proper security polices and to choose the proper tool in order to be more secure. In this study, using malware file set crawled from the Deep web we detect similarities and possible groupings between plethora of anti-virus tools (AVTs) that exist on the market. Moreover, using graph theory, data science and visualization we find which of the existing AVTs has greater advantage in detecting malware over the other AVTs, in a sense that the AVT detects many unique. Finally, we propose a solution, for the given malware set, what is the best strategy for a company to defend against malwares if it uses a multi-scanning approach.

Keyword :  Malware, Community detection, Anti-virus engines, data science, multi-scanning approach.

For More Details :  https://airccj.org/CSCP/vol8/csit89713.pdf

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

SECURE STRATEGY FOR OPTICAL IMAGE ENCRYPTION SYSTEM BASED ON AMPLITUDE MODULATION, PHASE MODULATION AND MODIFIED LOGISTIC MAP

Author :  Ahmed M. Elshamy

Affiliation :  Department of Network and Security, College of Information Technology, Fujairah University

Country :  Egypt

Category :  Computer Science & Information Technology

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

Abstract :

This paper presents an optical image encryption system based completely on amplitude modulation, phase modulation in the discrete Fourier transform and modified chaotic logistic map. Amplitude modulation and phase modulation are accomplished by the use of spatial light modulator (SLM). SLMs are normally used to control incident light in amplitude-best, phasebest or the mixture (amplitude-phase). The random amplitude modulation based on a chaotic Baker map is carried out in time domain, while the random phase modulation is accomplished in the frequency domain. In this paper, we proposed a technique to regulate and enhance protection in a chaotic logistic map method leading to increased variety of key space of the logistic map. This causes our encryption system to become exceptionally sturdy against brute pressure. An exhaustive analysis of the proposed encryption system is undergone and shows positive results in encryption metrics when compared to several different photo encryption techniques. The analysis demonstrates the highly valued security and immunity to noise of the photograph encryption. The proposed modified logistic map with amplitude and phase modulation is suitable for real-time application.

Keyword :  Image Encryption, Fourier Transform, Security, Chaotic Logistic Map, Chaotic Baker Map

For More Details :  https://airccj.org/CSCP/vol8/csit89808.pdf

PREDICTING SECURITY CRITICAL CONDITIONS OF CYBER PHYSICAL SYSTEMS WITH UNOBSERVABLES AND OBSERVATION TIMES

 Author :  Alessio Coletta

Affiliation :  Security and Trust Unit, Bruno Kessler Foundation

Country :  Italy

Category :  Computer Science & Information Technology

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

Abstract :

Cyber Physical Systems (CPS), like IoT and industrial control systems, are typically vulnerable to cyber threats due to a lack of cyber security measures and hard change management. Security monitoring is aimed at improving the situational awareness and the resilience to cyber attacks. Solutions tailored to CPS are required for greater effectiveness. This work proposes a monitoring framework that leverages the knowledge of the system to monitor in order to specify, check, and predict known critical conditions. This approach is particularly suitable to CPS, as they are designed for a precise purpose, well documented, and predictable to a good extent. The framework uses a formal logical language to specify quantitative critical conditions and an optimisation SMT-based engine that checks observable aspects from network traffic and logs. The framework computes a quantitative measure of the criticality of the current CPS system: checking how criticality changes in time enables to predict whether the system is approaching to a critical condition or reaching back a licit state. An important novelty of the approach is the capability of expressing conditions on the time of the observations and of dealing with unobservable variables. This work presents the formal framework, a prototype, a testbed, and first experimental results that validate the feasibility of the approach.

Keyword :  Security Monitoring, Detection and Prevention Systems, Critical Infrastructures, Cyber Physical Systems, SMT.

For More Details :  https://airccj.org/CSCP/vol8/csit89807.pdf

CAMOUFLAGED WITH SIZE: A CASE STUDY OF ESPIONAGE USING ACQUIRABLE SINGLE-BOARD COMPUTERS

 Author :  Kiavash Satvat

Affiliation :  University of Illinois at Chicago

Country :  USA

Category :  Computer Science & Information Technology

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

Abstract :

Single-Board Computers (SBC) refer to pocket-sized computers built on a single circuit board. A number of studies have explored the use of these highly popular devices in a variety of domains, including military, agriculture, healthcare, and more. However, no attempt was made to signify possible security risks that misuse of these devices may bring to organizations. In this study, we perform a series of experiments to validate the possibility of using SBCs as an espionage gadget. We show how an attacker can turn a Raspberry Pi device to an attacking gadget and benefit from short-term physical access to attach the gadget to the network in order to access unauthorized data or perform other malicious activities. We then provide experimental results of placing such tools in two real-world networks. Given the small size of SBCs, traditional physical security measures deployed in organizations may not be sufficient to detect and restrict the entrance of SBCs to their premises. Therefore, we reiterate possible directions for network administrators to deploy defensive mechanisms for detecting and preventing such attacks

Keyword :  Espionage, Single-Board Computer (SBC), Physical Security, Network Security, Raspberry Pi

For More Details :  https://airccj.org/CSCP/vol8/csit89806.pdf

DATA MANAGEMENT PLATFORM SUPPORTING VOLUNTEER OCCUPATION SERVICES

 Author :  Damien Nicolas

Affiliation :  LIST: Luxembourg Institute of Science and Technology

Country :  Luxembourg

Category :  Computer Science & Information Technology

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

Abstract :

This paper deals with the Data Management Services, which acts as a link between the User Requirements, the System Requirements and the development of the core IT software components supporting the occupation services performed during the Sponsor project. This gives an overview of the main services provided by the platform, the chosen architecture model including the relationships between the different conceptual models and the data models implemented within the platform. As a result, the outcomes of this deliverable will constitute an input for the development of the platform as a services-oriented platform providing and publishing a complete API for managing organizations, opportunities and volunteers.

Keyword :  Data Model, SOA, RESTful Web Services, IT service platform, Ambient Assisted Living, Graph Database, Volunteering Services.

For More Details :  https://airccj.org/CSCP/vol8/csit89805.pdf

VIRTUAL AIS GENERATION MANAGEMENT SYSTEM FOR WATERWAY RISK ASSESSMENT

Author :  Jun Sik Kim

Affiliation :  Division of Computer Engineering, Dongseo University

Country :  Korea

Category :  Computer Science & Information Technology

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

Abstract :

The virtual Automatic Identification System (AIS) generation management system is a system for analysing waterway risk by generating virtual AIS data which contains location information of a specific area. The system uses the data as an input data of the IALA Waterway Risk Assessment (IWRAP).

Keyword : Shipwreck, waterway risk assessment

URL :  https://airccj.org/CSCP/vol8/csit89804.pdf

FUZZY BOOLEAN REASONING FOR DIAGNOSIS OF DIABETES

 Author :  Mohamed Benamina

Affiliation :  Laboratoire d’Informatique d’Oran (LIO) University of Oran 1 Ahmed Ben Bella BP 1524, El M'Naouer Es Senia

Country :  Algeria

Category :  Computer Science & Information Technology

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

Abstract :

The classification by inductive learning finds its originality in the fact that humans often use it to resolve and to handle very complex situations in their daily lives. However, the induction in humans is often approximate rather than exact. Indeed, the human brain is able to handle imprecise, vague, uncertain and incomplete information. Also, the human brain is able to learn and to operate in a context where uncertainty management is indispensable. In this paper, we propose a Boolean model of fuzzy reasoning for indexing the monitoring sub-plans, based on characteristics of the classification by inductive learning. Several competing motivations have led us to define a Boolean model for CBR knowledge base systems. Indeed, we have not only desired experiment with a new approach to indexing of cases by fuzzy decision tree, but we also wanted to improve modelling of the vague and uncertain of the natural language concepts, optimize response time and the storage complexity.

Keyword :  Boolean Modelling, Cellular Machine, Case-Based Reasoning, Diabetes Diagnosis, Fuzzy Reasoning, Planning.

For More Details :  https://airccj.org/CSCP/vol8/csit89803.pdf

AN ONTOLOGY-BASED HIERARCHICAL BAYESIAN NETWORK CLASSIFICATION MODEL TO PREDICT THE EFFECT OF DNA REPAIRS GENES IN HUMAN AGEING PROCESS

 Author :  Hasanein Alharbi

Affiliation :  Department of Computer Engineering Techniques, Al-Mustaqbal University College

Country :  Iraq

Category :  Computer Science & Information Technology

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

Abstract :

Conventional Data Mining (DM) algorithms treated data simply as numbers ignoring the semantic relationships among them. Consequently, recent researches claimed that ontology is the best option to represent the domain knowledge for data mining use because of its structural format. Additionally, it is reported that ontology can facilitate different steps in the Bayesian Network (BN) construction task. To this end, this paper investigates the advantages of consolidating the Gene Ontology (GO) and the Hierarchical Bayesian Network (HBN) classifier in a flexible framework, which preserves the advantages of both, ontology and Bayesian theory. The proposed Semantically Aware Hierarchical Bayesian Network (SAHBN) is tested using data set in the biomedical domain. DNA repair genes are classified as either ageing-related or nonageing-related based on their GO biological process terms. Furthermore, the performance of SAHBN was compared against eight conventional classification algorithms. Overall, SAHBN has outperformed existing algorithms in eight experiments out of eleven.

Keyword :  Semantic Data Mining, Hierarchical Bayesian Network, Gene Ontology, DNA Repair Gene, Human Ageing Process

For More Details  :  https://airccj.org/CSCP/vol8/csit89802.pdf

ANDROID UNTRUSTED DETECTION WITH PERMISSION BASED SCORING ANALYSIS

Author :  JACKELOU SULAPAS MAPA

Affiliation :  College of Information Technology Saint Joseph Institute of Technology

Country :  Philippines

Category :  Computer Science & Information Technology

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

Abstract :

Android smart phone is one of the fast growing mobile phones and because of these it the one of the most preferred target of malware developer. Malware apps can penetrate the device and gain privileges in which it can perform malicious activities such reading user contact, misusing of private information such as sending SMS and can harm user by exploiting the users private data which is stored in the device. The study is about detecting untrusted on android applications, which would be the basis of all future development regarding malware detection. The smartphone users worldwide are not aware of the permissions as the basis of all malicious activities that could possibly operate in an android system and may steal personal and private information. Android operating system is an open system in which users are allowed to install application from any unsafe sites. However permission mechanism of and android system is not enough to guarantee the invulnerability of the application that can harm the user. In this paper, the permission scoring-based analysis that will scrutinized the installed permission and allows user to increase the efficiency of Android permission to inform user about the risk of the installed Android application, in this paper, the framework that would classify the level of sensitivity of the permission access by the application. The framework uses a formula that will calculate the sensitivity level of the permission and determine if the installed application is untrusted or not. Our result show that, in a collection of 26 untrusted application, the framework is able to correct and determine the application's behavior consistently and efficiently.

Keyword :  Permission, permission scoring-based, malware Android phone, Security, Internet, malware.

For More Details :  http://airccj.org/CSCP/vol8/csit89801.pdf

ENSEMBLE LEARNING BASED VOTING MODEL FOR DYNAMIC PROFILE CLASSIFICATION AND PROJECT ALLOTMENT

 Author :  Suhas Tangadle Gopalakrishna

Affiliation :  Infosys Limited, Bengaluru

Country :  India

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

Every year, lakhs of students right from college enter professional life through various recruitment activities conducted by the organization. The allotment of projects to the new recruits, carried out by the HR team of the organization is usually a manual affair. It is a time consuming and a tedious process as it involves manually opening each resume and analysing it one by one in order to assign a project. Companies round the globe are leveraging the power of artificial intelligence and machine learning to increase their productivity. In this paper, we present one such use case wherein artificial intelligence is leveraged by the organisation in allotment of projects to the new recruits. Current machine learning tools help in the allotment of projects to a few known popular domains on which the classifier has been trained explicitly. We tackle the problem with an ensemble learning based voting classifier consisting of 5 individual machine learning classifiers, voting to classify the profile of the candidate into the relevant domain. The knowledge extracted from the profiles for which there is no majority consensus among the individual classifiers is used to retrain the model. The proposed model achieves a higher accuracy in classifying resumes to proper domains than a standard machine learning classifier which is solely dependent on the training set for classification. Overall, emphasis is laid out on building a dynamic machine learning automation tool which is not solely dependent on the training data in allotment of projects to the new recruits.

Keyword :  Ensemble learning based voting classifier, Dynamic classification, Artificial Intelligence, Resume classifier, Association Rule Learning, Latent Dirichlet Allocation

For More Details :  https://airccj.org/CSCP/vol8/csit89607.pdf

OBJECT LOCALIZATION AND ACTIVITIES IDENTIFICATION USING ATTRIBUTE DETAILS IN SMART MEETING ROOMS

 Author :  Dian Andriana

Affiliation :  Research Center for Informatics, Indonesian Institute of Sciences

Country :  Indonesia

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

This paper is concerned with the development of interactive systems for smart meeting rooms. Automated recognition of video events is an important research area. We present an LTL (Linear Temporal Logic) model of basic objects and activities recognition in smart meeting rooms using object attribute details. There are still problems of misrecognizing objects in existing visual recognition methods because lack of enough feature attributive information details. This paper investigates morphological approach to increase recognition accuracy using variability in a limited area of moving object using object attribute details. The proposed methods are also compared to popular and recent methods of visual object and event recognition.

Keyword :  Events Recognition and Tracking, Morphological Feature Characteristics

For More Details :  https://airccj.org/CSCP/vol8/csit89606.pdf

A PAPR REDUCTION TECHNIQUE IN OFDM SYSTEMS WITH A LARGE NUMBER OF SUBCARRIERS

Author :  Yasuhiro Shimazu

Affiliation :  Department of Electrical Engineering, Tokyo University of Science

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

A major drawback of orthogonal frequency division multiplexing (OFDM) signals is extremely high peak-to-average power ratio (PAPR). Signals with high PAPR lead to a lowering of the energy efficiency of power amplifiers and the shortened operation time causes a serious problem in battery-powered wireless terminals. We have found the CAZAC precoding makes the PAPR of M-array quadrature amplitude modulation (M-QAM) OFDM signals into the PAPR of M-QAM single-carrier signals. Therefore, it can dramatically improve the PAPR of OFDM signals. However, to satisfy the 3GPP-LTE specification of frequency spectrum, severe bandpass filtering of CAZAC-OFDM signal lead to unacceptable regrowth of the PAPR. The paper provides available control procedure for PAPR and spectrum managements. It is confirmed that the CAZAC-OFDM signal controlled by our procedure maintains enough low PAPR and can provide comparable spectral specifications in the downlink channel of 3GPPLTE standard.

Keyword :  OFDM, CAZAC, PAPR, spectral specifications

For More Details :  https://airccj.org/CSCP/vol8/csit89605.pdf

IOT-BASED HOME APPLIANCE SYSTEM (SMART FAN)

Author :  Mehran Ektesabi

Affiliation :  Swinburne University of Technology

Country :  Thailand

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

Smart home appliances such as smart fridge, smart lighting, and smart air conditioner are getting popular for home end users. Smart fans as one of those smart devices are a part of a smart home that can be assumed as a factor of comfort, which may also reduce the electricity cost due to its high efficiency. Hence, this project aims to develop an alternative smart fan tackled from a comfort and cost perspectives. This project is done using as minimum budget as possible by using a combination of the already-available parts of the market. It is expected to develop a prototype of a cheap smart fan, which in turn becomes the starting point to allow further development of other smart home appliances

Keyword :  Internet of Things, Smart Fan, Motor Control, Cost Analysis, Cloud Infrastructure

For More Details :  https://airccj.org/CSCP/vol8/csit89604.pdf

TIME-DOMAIN SIGNAL MANAGEMENT FOR OFDM SIGNALS

 Author :  Takuya Kazama

Affiliation :  2Faculty of Engineering, Tokyo University of Science

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

We have found out that the CAZAC- OFDM accords the amplitude of IFFT output signal with the amplitude of input DATA and the time ordering of IFFT output signal is unambiguously determined. That is, the OFDM time-domain signals, which are composed of many sinewaves, can be shaped by CAZAC precoder. As one application example that can use this characteristic of CAZAC precoder, we propose a new technique of symbol timing estimation, which enable to avoid the use of the preambles and guard-intervals. Conventional OFDM systems introduce guard-intervals for symbol timing estimation and reduction of multipath effect. Visible light communications (VLCs), which are one kind of line-of–sight communications, does not require consideration of the multipath channel. Therefore, if we embed null data at a fixed position in time-domain, we will easily estimate the symbol timing in the receiver side.

Keyword :  OFDM; CAZAC sequence; Zadoff-Chu sequence; Symbol Timing Estimation; VLC.

For More Details :  https://airccj.org/CSCP/vol8/csit89603.pdf

TIME-DOMAIN SIGNAL MANAGEMENT FOR OFDM SIGNALS

 Author :  Takuya Kazama

Affiliation :  Faculty of Engineering, Tokyo University of Science

Country :  Japan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

We have found out that the CAZAC- OFDM accords the amplitude of IFFT output signal with the amplitude of input DATA and the time ordering of IFFT output signal is unambiguously determined. That is, the OFDM time-domain signals, which are composed of many sinewaves, can be shaped by CAZAC precoder. As one application example that can use this characteristic of CAZAC precoder, we propose a new technique of symbol timing estimation, which enable to avoid the use of the preambles and guard-intervals. Conventional OFDM systems introduce guard-intervals for symbol timing estimation and reduction of multipath effect. Visible light communications (VLCs), which are one kind of line-of–sight communications, does not require consideration of the multipath channel. Therefore, if we embed null data at a fixed position in time-domain, we will easily estimate the symbol timing in the receiver side.

Keyword :  OFDM; CAZAC sequence; Zadoff-Chu sequence; Symbol Timing Estimation; VLC.

For More Details :  https://airccj.org/CSCP/vol8/csit89603.pdf

IMPROVED BLOCK STAGEWISE REGULARIZED ORTHOGONAL MATCHING PURSUIT IMAGE RECONSTRUCTION METHOD

 Author :  Xiong-yong Zhu

Affiliation :  Department of Computer Science, Guangdong University of Education Guangzhou

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  8, 16, February, 2018

Abstract :

Traditional methods to signal acquisition need to collect large amounts of redundant data, and then compress the data to extract useful information, which is inefficient and requires large amount of storage resources. Compressed sensing (CS) can avoid sampling the redundant data; it obtains the discrete signals at the sampling rate that is lower than the Nyquist sampling rate, and reconstructs the original signal with high probability. Based on CS, Block Stagewise Regularized Orthogonal Matching Pursuit (StROMP) is proposed in this paper to reconstruct images. Simulation results show that the proposed algorithm can effectively reduce the required storage storages and computational complexity, which improves the quality of reconstructed images in the premise of ensuring a shorter reconstruction time.

Keyword :  Compressive Sensing; Matching Pursuit; Image Reconstruction

For More Details :  https://airccj.org/CSCP/vol8/csit89602.pdf