8th International Conference on Computer Science and Information Technology (CCSIT 2018)

November 24~25, 2018, Dubai, UAE

Accepted Papers


PROVER: AN SMT-BASED FRAMWORK FOR PROCESS VERIFICATION
Souheib Baarir, Reda Bendraou, Hakan Metin
Laboratoire d'Informatique de Paris 6, Paris, France

ABSTRACT

Business processes are used to represent the company's business and services it delivers. They are also means to create an added value to the company as well as to its customers. It is then more than critical to seriously consider the design of such processes and to make sure that they are free of any kind of inconsistencies. This paper introduces our new framework called ProVer. Three of its design decisions will be motivated: (1) the use of UML Activity Diagrams (AD) as a process modeling language, (2) the formalization of the UML AD concepts for process verification as well as a well-identified set of properties in first-order logic (FOL) and (3) the use of SMT (Satisfiability Modulo Theories) as means to verify properties spanning different process's perspectives in an optimal way. The originality of ProVer is the ability for non-experts to express properties to be verified on processes that span the control, data, time, and resource perspectives using the same tool.

ASK LESS-SCALE MARKET RESEARCH WITHOUT ANNOYING YOUR CUSTOMERS
Venkatesh Umaashankar1* and Girish Shanmugam S2
1Ericsson Research, Chennai, India.2Machine Learning Consultant, E3, Jains Green Acres, Chennai, India

ABSTRACT

Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and communication. Reducing the number of questions asked to the customer has utility for businesses to scale the market research to a large number of customers. In this work, we model this task using Bayesian networks. We demonstrate the effectiveness of our approach using an example market segmentation of broadband customers.

AN APPROACH FOR WEB APPLICATIONS TEST DATA GENERATION BASED ON ANALYZING CLIENT SIDE USER INPUT FIELDS
Samer Hanna1 and Hayat Jaber2
1Department of Software Engineering, Faculty of Information Technology, Philadelphia University, Jordan. 2Department of Computer Science, Faculty of Information Technology, Philadelphia University, Jordan

ABSTRACT

It is time consuming to manually generate test data for Web applications; therefore, automating this task is an important task for both practitioners and researchers in this domain. To achieve this goal, the research in this paper depends on an ontology that categorizes Web applications inputs according to input types such as number, text, and date. This research presents rules for Test Data Generation for Web Applications (TDGWA) based on the input categories specified by the ontology. Following this paper’s approach, Web applications testers will need less time and effort to accomplish the task of TDGWA. The approach had successfully been used to generate test data for different experimental and real life Web applications.

SEGMENTING RETAIL CUSTOMERS WITH ENHANCED RFM DATA USING AHYBRID REGRESSION/CLUSTERING METHOD
Fahed Yoseph, Professor Markku Heikkilä, Mohammed Malaily
Åbo Akademi University, Finalnd, Turku

ABSTRACT

Targeted marketing strategies attract interest from both industry and academia. A viable approach for gaining insight into the heterogeneity of customer purchase lifecycle is market segmentation. Conventional market segmentation models often ignore the evolution of customers’ behavior over time. Therefore, retailers often end up spendingtheir limited resources attempting to serve unprofitable customers. This study looks into the integration of Recency, Frequency, Monetary scoresand Customer Lifetime Value model, and applies the resulting data to segment customers of a medium-sized clothing and fashion accessory retailer in Kuwait. A modified regression algorithm is implemented for finding the slope for customer purchase curve. Then K-means and Expectation Maximization clustering algorithms are used to findthe sign of the curve. The purpose is to gain knowledgefrom point-of-sales data and help the retailer to make informed decisions. Cluster quality assessment concludes that the EM algorithm outperformed k-means algorithm in finding relevant segments. Finally, appropriate marketing strategies are suggested in accordance with the results generated by EM clustering algorithm

BIRD SWARM ALGORITHM FOR SOLVING THE LONG-TERM CAR POOLING PROBLEM
Zakaria BENDAOUD1 and Sidahmed BENNACEF2
1GeCode Laboratory, Department of Computer Science, Dr. Moulay Tahar University of Saida Algeria. 2Department of Computer Science, Moulay Tahar University of Saida Algeria

ABSTRACT

Carpooling consists in sharing personal vehicles to make a joint trip, in order to share the costs of fuel, toll (soon in Algeria) or simply to exchange. The purpose of this work is to benefit from web 2.0 tools in order to adopt the ideal strategy for carpooling we treated the family of long-term carpool, the problem is to find the best groups between a set of individuals who make the same trip every day and in a regular way. in order to reach our goal, we adapted a bio-inspired meta-heuristics, this technique allowed us to have very satisfying results.

VISUALISATION OF MULTI-SERVICE SYSTEM NETWORK WITH D3.JS & KDB+/Q USING WEBSOCKET
Ali Kapadiya
Kdb+ Tick-data and Analytics developer, London, UK

ABSTRACT

Visualisation of complex web of services running in a multi-service system using D3.js as frontend, KDB+/q as backend and WebSocket & JSON for communication

AUDIO ENCRYPTION ALGORITHM USING HYPERCHAOTIC SYSTEMS OF DIFFERENT DIMENSIONS
S. N. Lagmiri1 and H. Bakhous2
1,2IRSM, Higher Institute of Management Administration and Computer Engineering Rabat, Morocco

ABSTRACT

Data security has become an important concern for communication through an insecure channel because the information transferred across the networks has a large chance of unauthorized access. The available encryption algorithms that are primarily used for text data may not be suitable for multimedia data such as sound. Hyperchaotic systems are generally proposed as a solution to multimedia encryption, because of their random properties and the high sensitivity of initial conditions and system parameters. In this paper, audio data encryption with different dimensional hyperchaotic systems has been presented. The proposed hyperchaotic systems exhibit excellent chaotic behavior. To demonstrate its application to the processing of multimedia encryption, the three systems are applied with an algorithm based on the key generation from the initial conditions for encryption and decryption process. The results of encryption, decryption and statistical analysis of the audio data show that the proposed cryptosystem has excellent encryption performance, high sensitivity to security keys and can be applied for secure real-time encryption.


PROPOSAL OF A PUBLISH-SUBSCRIBE MODEL FOR COMMUNICATION IN VANET
Mohamed Anis MASTOURI and Salem Hasnaoui
Communication systems – Sys’Com Laboratory National school of engineers of Tunis - ENIT Tunis, Tunisia

ABSTRACT

Researchers in Vehicular ad hoc networks has focused mainly on design of routing protocols in the context of closely spaced vehicles. However the network of VANET can either be sparsely connected depending on the time of day. In sparse vehicular network there are a lot of chances to drop the communication. So there is necessary to avoid the discontinu-ity during the data exchange in this kind of network.In this paper we propose the technique of caching with the store carry and forward algorithm using publish-subscribe communi-cation paradigm which provides decoupling in time, space and synchronization between communicating entities, making it most suitable for VANET like environments. This solution is designed in order to bypass discontinuity and high mobility.


RESEARCH ON CRO'S DILEMMA IN SAPIENS CHAIN: A GAME THEORY METHOD
Jinyu Shi1, Zhongru Wang1,2, Qiang Ruan3, Yue Wu1and Binxing Fang1
1Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China, 2Zhejiang Lab, Hangzhou, China, 3Beijing DigApis Technology Co., Ltd, Beijing, China

ABSTRACT

In recent years, blockchain-based techniques have been widely used in cybersecurity, owing to the decentralization, anonymity, credibility and not be tampered properties of the blockchain. As one of the decentralized framework, Sapiens Chain was proposed to protect cybersecurity by scheduling the computational resources dynamically, which were owned by Computational Resources Owners (CROs). However, when CROs in the same pool attack each other, all CROs will earn less. In this paper, we tackle the problem of prisoner’s dilemma from the perspective of CROs. We first define a game that a CRO infiltrates another pool and perform an attack. In such game, the honest CRO can control the payoffs and increase its revenue. By simulating this game, we propose to apply Zero Determinant (ZD) strategy on strategy decision, which can be categorized into cooperation and defecting. Our experimental results demonstrate the effectiveness of the proposed strategy decision method.


DIGITAL INFRARED IMAGING FOR BREAST CANCER DETECTION USING SEQUENTIAL MINIMAL OPTIMIZATION, KERNEL LOGISTIC REGRESSION AND MULTILAYER PERCEPTRON
Shaimaa Adel Abd El-Halim1, Nader Abd El-Rahman Mohamed2, AmrAbd El-RahmanSharawy3
1,2Misr University for Science and Technology, Faculty of Engineering, 3Cairo University, Faculty of Engineering

ABSTRACT

In Egypt, breast cancer is the most common cancer among females. Death percentage by breast cancer is up to 37.7% out of 12.621 new cases in 2008. Breast cancer starts when cells in the breast begin to grow out of control. However, there are many techniques help to discover the cancer in a more safe way. One of them is Digital Infrared Imaging (thermography); it is based on the metabolic activation and vascular circulation in both pre-cancerous tissue and the cancerous one. This paper proposes a method for breast cancer detection that uses image processing techniques. These techniques are applied to 142 breast digital thermal images; 77 of them are normal images and 65 are abnormal ones. Matlab is used for detecting region of interest (ROI) and feature extraction. In addition, Weka is used for classification and we used three type of classification Sequential Minimal Optimization (SMO), Kernel Logistic Regression, and Multilayer Perceptron. In the (ROI) extraction phase, active contour techniques are used, and then 72 statistical and textural features are extracted. Finally, used this features to feed the classifier, which gave us an accuracy of 99.29% using Sequential Minimal Optimization (SMO), 98.59% using Kernel Logistic Regression, and 96.4 using Multilayer Perceptron.


INTERNET OF THINGS-BASED INFORMATION SYSTEM FOR SMART WIRELESS SENSOR HEALTHCARE APPLICATION: DESIGN METHODOLOGY APPROACH
Polash Kumar Das1, Prabhat Ranjan2 and Farhad Banoori3
1,2Masters Student,South China University of Technology,Guangzhou, China 3Ph.d Student,South China University of Technology,Guangzhou, China

ABSTRACT

With the advent of awareness toward quality of life in people across the world has kindled a widespread investment and concern in science community for a better biomedical product, research and new technology. Biomedical health is no longer limited to pharmaceutical drugs but monitoring of daily body vitals for prevention and improved diagnostics is getting a lot of attentions. In recent years, wireless technology has found many uses in biomedical industry. In this paper, we are going to use wireless body area network technology for monitoring a patient’s condition in a given system. Wireless body area network has wide range of research application from indoor positioning to patient fall monitoring, sleep monitoring and heart beat recording. With emerging practical use of wireless body area network, scientists have proposed many innovative ways for health and body vitals monitoring such as channel state information (CSI) and receiving signal strength indication (RSSI).The simple architecture and lower cost of retrieving RSSI data had been published by many researchers. However, there is two major shortcomings for RSSIbased research methods. (1) The multipath effect for indoor system increasing the RSSI fluctuations and vast errors. (2) Since RSSI is coarse information, its subcarrier multipath information cannot be studied.
CSI can characterize the multipath propagation of signal to some extent in comparison to RSSI method. In this paper, we propose a system design for identification of a particular disease namely, narcolepsy, combining wireless communication technology and computer science analytics. This system setup continuously transmits a particular frequency signal and the receiver obtains the reflected signal containing the patient’s information in a particular environment with changing body postures in real time. The various path gain data collected is used to extract and analyze characteristics of the patient position characterizing the disease of narcolepsy and eventually achieving the goal of monitoring.


SAPIENS CHAIN: A BLOCKCHAIN-BASED CYBERSECURITY FRAMEWORK
Yu Han1, Zhongru Wang1,2, Qiang Ruan3, Binxing Fang1and Lei Zhao4
1Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China, 2Zhejiang Lab, Hangzhou, China, 3Beijing DigApis Technology Co., Ltd, Beijing, China, 4Chinese Academy of Cyberspace Studies, Beijing, China

ABSTRACT

Recently, cybersecurity becomes more and more important due to the rapid development of Internet. However, existing methods are in reality highly sensitive to attacks and are far more vulnerable than expected, as they are lack of trustable measures. In this paper, to address the aforementioned problems, we propose a blockchain-based cybersecurity framework, termed as Sapiens Chain, which can protect the privacy of the anonymous users and ensure that the transactions are immutable by providing decentralized and trustable services. Integrating semantic analysis, symbolic execution, and routing learning methods into intelligent auditing, this framework can achieve good accuracy for detecting hidden vulnerabilities. In addition, a revenue incentive mechanism, which aims to donate participants, is built. The practical results demonstrate the effectiveness of the proposed framework.

WHY WE NEED A NOVEL FRAMEWORK TO INTEGRATE AND TRANSFORM HETEROGENEOUS MULTI-SOURCE GEO-REFERENCED INFORMATION: THE J-CO PROPOSAL
Gloria Bordogna1 and Giuseppe Psaila2
1CNR IREA - Via Bassini 15 - 20133 Milano - Italy, 2University of Bergamo - Viale Marconi 5 – 24044 Dalmine (BG) - Italy

ABSTRACT

The large number of geo referenced data sets provided by Open Data portals, social media networks and created by volunteers within citizen science projects (Volunteered Geographical Information) is pushing analysts to define and develop novel frameworks for analysing these multisource heterogeneous data sets in order to derive new data sets that generate social value. For analysts, such an activity is becoming a common practice for studying, predicting and planning social dynamics. The convergence of various technologies related with data representation formats, database management and GIS (Geographical Information Systems) can enable analysts to perform such complex integration and transformation processes. JSON has become the de-facto standard for representing (possibly geo-referenced) data sets to share; NoSQL databases (and MongoDB in particular) are able to natively deal with collections of JSON objects; the GIS community has defined the GeoJSON standard, a JSON format for representing georeferenced information layers, and has extended GIS software to support it.
However, all these technologies have been separately developed, consequently, there is actually a gap that shall be filled to easily manipulate GeoJSON objects by performing spatial operations. In this paper, we pursue the objective of defining both a unifying view of several NoSQL databases and a query language that is independent of specific database platforms to easily integrate and transform collections of GeoJSON objects. In the paper, we motivate the need for such a framework, named J-CO, able to execute novel high-level queries, written in the J-CO-QL language, for JSON objects and will show its possible use for generating open data sets by integrating various collections of geo-referenced JSON objects stored in different databases.


The Reuse Challenge in Evolutionary Computing, The Added Value of Software Product Lines
Abdelghani Alidra1 and Mohamed Tahar Kimour2
120 aout 55 University, Algeria and 2Badji Mokhtar University, Algeria

ABSTRACT

Evolutionary computing (EC) designates the computing science discipline involved in developing biology inspired algorithms for solving hard search-based problems. Evolutionary computing suites well to various engineering problems and has been successfully applied to many of them. Adopting EC in practice, however, has uncovered several challenging issues, such as the efficient reuse of the evolutionary code, the correct tuning of the algorithm and the dynamic evolution of its behavior to balance divergent requirements. To address these issues, we propose in the present article, a new approach to evolutionary algorithms development based on product line engineering. Our approach is centered on the feature model of the evolutionary algorithms software family. This is notable in that it offers a rigorous way to the relevant identification and implementation of the reusable parts. Moreover, it allows the exploitation of existing model-based techniques for automatic code generation and reasoning. It also opens promising perspectives to the intelligent tuning and dynamic reconfiguration of the evolutionary algorithm through the exploitation of the most recent advances in the field of dynamic software product lines.


Detecting Chronic Diseases from Sleep-Wake Behaviour and Clinical Features
Sarah Fallmann and Liming Chen
De Montfort University, UK

ABSTRACT

Many chronic diseases show evidence of correlations with sleep-wake behaviour, and there is an increasing interest in making use of such correlations for early warning systems. This research presents an approach towards early chronic disease detection by mining sleep-wake measurements using deep learning. Specifcally, a Long-Short-Term-Memory network is applied on actigraph data enriched with clinical history of patients. Experiments and analysis are performed targeting detection at an early and advanced disease stage based on diferent clinical data features. The results show for disease detection an averaged accuracy of 0:62, 0:73, 0:81, 0:77 for hypertension, diabetes, sleep apnea and chronic kidney disease, respectively. Early detection performs with an averaged accuracy of 0:49 for sleep apnea and 0:56 for diabetes. Nevertheless, compared to existing work, our approach shows an improvement in performance and demonstrates that predicting chronic diseases from sleep-wake behavior is feasible, though further investigation will be needed for early prediction.


Artificial Intelligence-Fusing the Future in Medical Cannabis Production
Everton H. Flemmings
AOE Gropu of Companis Ltd, UK

ABSTRACT

The active compounds in cannabis are called cannabinoids, and there are at least 85 of them, of which the most commonly discussed are tetrahydrocannabinol (THC) and cannabidol (CBD). While the majority of states in the USA have opted to legalise THC for either medical (that is , with prescription for a limited range of conditions) or adult usage, some have chosen to instead allow CDBD usage only as is not psychoactive, meaning it doesn’t provide the traditional high associated with cannabis. Low THC/high CBD laws are commonly viewed as a way to allow access to legal cannabis for the neediest patients, such as children with seizure disorders, without creating a fully fledge industry in the state.


COMPARISONOF FOURALGORITHMS FOR ONLINECLUSTERING
Xinchun Yang1,2, Wassim Kabbara1,3
1Department of Computer Engineering, CentraleSupelec, Paris, France. 2Department of Electrical Engineering, Tsinghua University, Beijing, China. 3Department of Electrical Engineering, Tsinghua University, Beijing, China

ABSTRACT

This paper concludes and analyses four widely-used algorithms in the field of online clustering: sequential K-means, basic sequential algorithmic scheme, online inverse weighted K-means and online K-harmonic means. All algorithms are applied to the same set of self-generated data in 2-dimension plane with and without noise separately. The performance of different algorithms is compared by means of velocity, accuracy, purity,and robustness. Results show that the basic sequential K-means online performs better on data without noise, and the K-harmonic means online performs is the best choice when noise interferes with the data.


PERCEPTUAL MAPPING OF ELECTRONIC BANKING IN IRAN: DATA MINING APPROACH
Sina Fakharmanesh1
1Faculty of Management,University of Shahid Beheshti, Tehran, Iran

ABSTRACT

Electronic banking has been on rise in recent years and this growth is still traceable in developing countries by increase of internet usage. Early studies in this realm explored the influential factors on adapting this new concept by bank customers. There is paucity of research digging deep on customer perception regarding internet banking. The aim of this study is to extend this area of investigation by exploring perceptual map of electronic banking in Iran. For fulfilling this purpose ten most popular banks of Iran are chosen and by use of principal component analysis dimensions of their perception were analyzed. Results showed that four clusters are detectable among customers which are website functionality, user satisfaction, security and fulfillment. Mangerial implications and directions for future research are presented at the final stage of this article.


IMPUTING ITEM AUXILIARY INFORMATION IN NMF-BASED COLLABRATIVE FILTERING
Fatemah Alghamedy1, Maryam Al-Ghamdi2 Jun Zhang, Ph.D3
1Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA. 2Department of Computer Science, University of Jeddah, Jeddah, Saudi Arabia 3Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA

ABSTRACT

The cold-start items, especially the New-Items which did not receive any ratings, have negative impacts on NMF (Nonnegative Matrix Factorization)-based approaches, particularly the ones that utilize other information besides the rating matrix. We propose an NMF based approach in collaborative filtering based recommendation systems to handle the New-Items issue. The proposed approach utilizes the item auxiliary information to impute missing ratings before NMF is applied. We study two factors with the imputation: (1) the total number of the imputed ratings for each New-Item, and (2) the value and the average of the imputed ratings. To study the influence of these factors, we divide items into three groups and calculate their recommendation errors. Experiments on three different datasets are conducted to examine the proposed approach. The results show that our approach can handle the New-Item’s negative impact and reduce the recommendation errors for the whole dataset.


ENHANCE NMF-BASED RECOMMENDATION SYSTEMS WITH SOCIAL INFORMATION IMPUTATION
Fatemah Alghamedy1and Jun Zhang, Ph.D2
1Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA. 2Department of Computer Science, University of Jeddah, Jeddah, Saudi Arabia

ABSTRACT

We propose an NMF (Nonnegative Matrix Factorization)-based approach in collaborative filtering based recommendation systems to improve the Cold-Start-Users predictions since Cold-Start-Users suffer from high error in the results. The proposed method utilizes the trust network information to impute a subset of the missing ratings before NMF is applied. We proposed three strategies to select the subset of missing ratings to impute in order to examine the influence of the imputation with both item groups: Cold-Start-Items and Heavy-Rated-Items; and survey if the trustees’ ratings could improve the results more than the other users. We analyze two factors that may affect results of the imputation:(1)the total number of imputed ratings, and (2) the average of imputed ratings value Experiments on four different datasets are conducted to examine the proposed approach. The results show that our approach improves the prediction rating of the cold-start users and alleviates the impact of imputed ratings

Visual Categorization of Objects into Animal and Plant Groups Using Global Shape Descriptors
Zahra Sadeghi
Department of Electrical and Computer Engineering, University of Tehran, Iran
Computer Vision Center, Universitat Autonomous de Barcelona (UAB), Spain


ABSTRACT

How can humans distinguish between general categories of objects? Are the subcategories of living things visually distinctive? In a number of semantic-category deficits, patients are good at making broad categorization but are unable to remember fine and specific details. It has been well accepted that general information about concepts are more robust to damages related to semantic memory. Results from patients with semantic memory disorders demonstrate the loss of ability in subcategory recognition. While bottom-up feature construction has been studied in detail, little attention has been served to top-down approach and the type of features that could account for general categorization. In this paper, I show that broad categories of animal and plant are visually distinguishable without processing textural information. To this aim I utilize shape descriptors with an additional phase of feature learning. The results are evaluated with both supervised and unsupervised learning mechanisms. The obtained results confirmed that global encoding of visual appearance of objects accounts for high discrimination between animal and plant object categories


Rising Threat Of Mobile Phone As Mobile Health Equipment In African Countries
Ramadile Isaac Moletsane 1 and Keneilwe Zuva2
1 Department of Software Studies, Vaal University of Technology, Vanderbijlpark, South Africa
2University of Botswana, Gaborone, Botswana


ABSTRACT

The widespread introduction of mobile devices has made enabling conditions for the deployment of mobile health activities. Although mobile health is a relatively new concept it is transforming healthcare all over the world. It is a rapidly progressing area with tremendous rate. Fifteen publications were identified from Elsevier, PubMed and Google scholar databases and specific to the purpose of this paper. The search was restricted to humans, date of publication (2014 to 2017) and publication language (English). The aim of this narrative review paper was to analyse possible hazards and benefits of mobile phones as mobile health equipment to the environment and wellbeing respectively and suggest an intervention. Mobile phones were found to be the most mHealth equipment used in Africa. The continent is realizing the benefits from mHealth practices. The issue concerning about mobile phones when they reach their end-of-life is their toxicity to the environment and wellbeing. Africa is found to manage electronic waste in a manner that is not friendly to the environment. Therefore the study suggests that awareness of detrimental effects of this waste be prioritized.


Rate Control Method For Near-Lossless Image Compression with JPEG-Ls
Shigao Li
School of Mathematic & Computer Science, Wuhan Polytechnic University, Wuhan Hubei, China

ABSTRACT

JPEG-LS become the standard of lossless and near-lossless image compression because of its performance and low complexity. However, it can't accurately control code rate when it is applied in near-lossless compression. This paper is thus devoted to rate control for near-lossless image compression with JPEG-LS. A model of coding bit-rate under a high bit-rate with respect to mean absolute difference (MAD) and coding quantization parameters for prediction coding is first proposed. Then a rate control method for near-lossless compression is designed based on the model for JPEG-LS. In the process of a specific image coding, to control the bit-rate, quantitative parameters are adjusted piecewise based on the model. Experiments show that the proposed method can make final code rate close to a preset rate. It's different from other methods that quantitative parameter fluctuating within a wide range can be avoided because of the accurate model of bit-rate. As a result, the proposed control method can achieve approximate optimal rate-distortion performance.


Microscopic Image Compression Using Support Vector
Chahinez Meriem Bentaouza1, 2 and Mohamed Benyettou1
1Department of Computer Science, Faculty of Mathematics and Computer Science, University of Sciences and Technology of Oran, Oran, Algeria
2Department of Mathematics and Computer Science, Faculty of Exact Sciences and Computer Science, University of Mostaganem, Mostaganem, Algeria


ABSTRACT

This paper deals constitution of compressed image after learning by support vector machines applied to microscopic images. The compression is used to reduce medical image size defined by an important acquisition for each exam, so, big size for storage and a lot of time for transmission. The compression ratio is satisfactory, but the result image is different from the original image because the compressed image has only support vectors, so we have loss of visual information.

Learning Trajectory Patterns by Sequential Pattern Mining From Probabilistic Databases
Josky Aizan2, Cina Motamed2 and Eugene C. Ezin3
1Ecole Doctorale Sciences Exactes et Appliquees
22Laboratoire d'Informatique Signal et Image de la Cote d'OpaleUniversite du LittoralCote d'Opale, France
3Institut de Mathematiques et de Sciences Physiques Universite d'Abomey-Calavi, Benin


ABSTRACT

In this paper, we use Sequential Pattern Mining from Probabilistic Databases to learn trajectory patterns. Trajectories which are a succession of points are firstly transformed into a succession of zones by grouping points to build the symbolic sequence database. For each zone we estimate a confidence level according to the amount of observations appearing during trajectory in the zone. The management of this confidence allows to reduce efficiently the volume of useful zones for the learning process. Finally, we applied a Sequential Pattern Mining algorithm on this probabilistic databases to bring out typical trajectories.


A Machine Fault Detection andDiagnosis System Using Sound-to-Image Conversion Feature Representation
Caleb Vununu1, Ki-Ryong Kwon1
1Dept. of IT Convergence and Applications Engineering,Pukyong National University, Busan, Korea

ABSTRACT

The present work proposes a sound-based machine fault detection system for the assessment of the drilling machines in industry sites. The main contribution of this work is to represent the sounds as images and then apply on the newly created images some transformations in order to reveal the hidden heath patterns originally absent in the sounds. The sounds are first recorded from faultless and defective drills for the analysis. The recorded sounds are converted to 8-bit grayscale images by using some 1D-to-2D transformations. Secondly, after a contrast enhancement process carried out to correct the poor contrast of the images, a low-pass filtering in the spatial domain is applied to the images in order to attenuate their gray variation. The filtered images are used as the features for the diagnosis assessment. A final step consists of feeding the images to a nonlinear classifier whose outputs will be the final assessment decision. We demonstrate that the proposed feature extraction method seize and reveals the health patterns carried out by the sounds.