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