Saad A.,CNRS Research Group of Electrotechnics and Automatics of Le Havre |
Zaarour I.,Lebanese University |
Guerin F.,CNRS Research Group of Electrotechnics and Automatics of Le Havre |
Bejjani P.,Holy Spirit University of Kaslik |
And 2 more authors.
International Journal of Machine Learning and Cybernetics | Year: 2017
Freezing of Gait (FoG) in Parkinson Disease (PD) is a sudden episode characterized by a brief failure to walk. The aim of this study is to detect FoG episodes using a multi-sensor device for data acquisition, and Gaussian neural networks as a classification tool. Thus we have built a multi sensor prototype that detects FoG using new indicators like the variation of the inter-foot distance or the knee angle. Data are acquired from PD patients having FoG as a major symptom. The major social challenge is obtaining the acknowledgment of patients to participate in our study, whereas the main technical difficulty is extracting efficient features from various walking behaviors. For that purpose, the acquired signals are analyzed in order to extract both time and frequency domain features that separate the FoG class from the other gaits modes. Due to the complexity of FoG episodes, the optimal features are then extracted using Principal Component Analysis technique. Another contribution is to introduce the combined data into the Gaussian Neural Network (GNN) classification method, that is a new technique used for FoG detection, and has been developed in our previous works. Moreover, the classical thresholding method is implemented to compare and validate the GNN method. Results showed the feasibility of integrating the chosen sensors, in addition to the effectiveness of combining data from different types of sensors on the classification rate. The efficiency rate of classification in the proposed method is about 87 %. © 2015, Springer-Verlag Berlin Heidelberg.
Khazaal Y.,University of Geneva |
Chatton A.,University of Geneva |
Atwi K.,Islamic University of Lebanon |
Zullino D.,University of Geneva |
And 2 more authors.
Substance Abuse: Treatment, Prevention, and Policy | Year: 2011
Background: The popularity of using the Internet and related applications has grown in Arabic countries in recent years. Despite numerous advantages in terms of optimizing communications among individuals and social systems, the use of the Internet may in certain cases become problematic and engender negative consequences in daily life. As no instrument in the Arabic language is available, however, to measure excessive Internet use, the goal of the current study was to validate an Arabic version of the Compulsive Internet Use Scale (CIUS).Methods: The Arabic version of the CIUS was administered to a sample of 185 Internet users and exploratory and confirmatory analyses performed.Results: As found previously for the original version, a one-factor model of the CIUS had good psychometric properties and fit the data well. The total score on the CIUS was positively associated with time spent online.Conclusion: The Arabic version of the CIUS seems to be a valid self-report to measure problematic Internet use. © 2011 Khazaal et al; licensee BioMed Central Ltd.
Faez A.,Islamic Azad University at Semnan |
Ahmadvand Z.,Allame Tabatabaee University |
Mirzaei M.,Islamic Azad University at Semnan |
Ahmadvand S.,Islamic University of Lebanon
Advances in Environmental Biology | Year: 2014
Background: Investors search for profitable investment opportunities with respect to the risk in financial markets. Since investors are willing to sell stocks whenever they want; they invest in liquid stocks with low liquidity risk. The free float is an important criterion that can show liquidity of a single stock. The higher the stock free float, the lower liquidity risk. There is a common sense that the higher the stock free float, the higher the transaction volume and so the higher the risk and also the higher the expected return. Objectives: This paper investigates the relationship between risk, return, liquidity and free float of shares in the companies listed in Tehran stock exchange. In this regard, the research hypnosis's was developed to purpose that there is a significant relationship between risk, return and liquidity with float of shares in the companies listed in Tehran stock exchange. For this purpose the statically society was all firms in Tehran Stock Exchange and the financial information was gathered with use of their financial reports from 2007 and ending to 2013. Results: The research hypothesis was examined using SPSS and E-Views as well as t-test. The results show that, there is a significant relationship between risk, return and liquidity with free float of shares. © 2014 AENSI Publisher All rights reserved.
Danach K.,Islamic University of Lebanon |
Khalil W.,Lebanese University |
Gelareh S.,University of Artois
2015 3rd International Conference on Technological Advances in Electrical, Electronics and Computer Engineering, TAEECE 2015 | Year: 2015
The service network design problems arising in liner shipping industry are very intractable problems. Several exact method are proposed for such problems where almost all of them are limited by the instance size that can be resolved. In this article, we consider the problem of designing multiple strings among a set of ports, in order to maximize the industry profit. In this work, we develop hyper-heuristics by proposing different low level heuristics categorized as constructive, improvement, perturbation etc. The low level heuristics are guided by a meta-heuristic algorithm that is supported by data mining techniques to attain balancing between intensification and diversification strategies in choosing the best heuristics series to be applied. © 2015 IEEE.
Barakat M.,University of Le Havre |
Barakat M.,University of Tripoli |
Druaux F.,University of Le Havre |
Lefebvre D.,University of Le Havre |
And 2 more authors.
Neurocomputing | Year: 2011
Fault detection and diagnosis have gained widespread industrial interest in machine monitoring due to their potential advantage that results from reducing maintenance costs, improving productivity and increasing machine availability. This article develops an adaptive intelligent technique based on artificial neural networks combined with advanced signal processing methods for systematic detection and diagnosis of faults in industrial systems based on a classification method. It uses discrete wavelet transform and training techniques based on locating and adjusting the Gaussian neurons in activation zones of training data. The learning (1) provides minimization in the number of neurons depending on cost error function and other stopping criterions; (2) offers rapid training and testing processes; (3) provides accuracy in classification as confirmed by the results on real signals. The method is applied to classify mechanical faults of rotary elements and to detect and isolate disturbances for a chemical process. Obtained results are analyzed, explained and compared with various methods that have been widely investigated for fault diagnosis. © 2011 Elsevier B.V.
Mcheick H.,University of Quebec at Chicoutimi |
Mohammed Z.R.,Islamic University of Lebanon |
Lakiss A.,Lebanese University
Proceedings - 2011 9th International Conference on Software Engineering Research, Management and Applications, SERA 2011 | Year: 2011
The Information technology Infrastructure plays an important role in the success of business applications. However, these applications suffer from performance and availability. In this vain, resource utilization is out of balance. Load balancing is very important approach to minimize the execution time because it has many processes units that are running in the same time. It is important to decompose the tasks among processors to achieve load balance. We distinguish two approaches to solve load balance: static and dynamic. Each approach has many algorithms which are not yet evaluated to understand the advancements as well as weaknesses over each other. The main purpose of this paper is to help in design of new algorithms in future by studying the behavior of various existing algorithms. We are going to evaluate these algorithms based on new parameters such as process migration, overhead, scalability and availability. © 2011 IEEE.
Saleh M.,Islamic University of Lebanon |
Faour G.,National Center for Remote Sensing
Proceedings of the 18th Mediterranean Electrotechnical Conference: Intelligent and Efficient Technologies and Services for the Citizen, MELECON 2016 | Year: 2016
Snow Cover Area monitoring is an important factor in studies of global climate change, regional water balance and soil moisture. Recently, the usage of remote sensing techniques has flourished. In fact, remote sensing data provides timely adequate snow cover information for large areas. While the National Center for Remote Sensing in Lebanon (CNRS) has recently established an operational monitoring room for natural resources and natural disasters, this paper presents the implementation of a fully automated snow cover monitoring system based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The system uses snow products from EOS Terra, and Aqua satellites to monitor the Snow Cover of Lebanon during the snow season (i.e. November-April). The importance of this project lies in its daily and fully automated process of acquiring, processing, storing and displaying statistics of the snow covered areas in Lebanon. Applying a custom algorithm based on combining Terra and Aqua snow products will reduce cloud contamination. © 2016 IEEE.
Harris N.,American University of Beirut |
Badr L.K.,Azusa Pacific University |
Saab R.,American University of Beirut |
Khalidi A.,Islamic University of Lebanon
Journal of Pediatric Oncology Nursing | Year: 2014
Medication errors (MEs) are reported to be between 1.5% and 90% depending on many factors, such as type of the institution where data were collected and the method to identify the errors. More significantly, the risk for errors with potential for harm is 3 times higher for children, especially those receiving chemotherapy. Few studies have been published on averting such errors with children and none on how caregivers perceive their role in preventing such errors. The purpose of this study was to evaluate pediatric oncology patient's caregivers' perception of drug administration safety and their willingness to be involved in averting such errors. A cross-sectional design was used to study a nonrandomized sample of 100 caregivers of pediatric oncology patients. Ninety-six of the caregivers surveyed were well informed about the medications their children receive and were ready to participate in error prevention strategies. However, an underestimation of potential errors uncovered a high level of "trust" for the staff. Caregivers echoed their apprehension for being responsible for potential errors. Caregivers are a valuable resource to intercept medication errors. However, caregivers may be hesitant to actively communicate their fears with health professionals. Interventions that aim at encouraging caregivers to engage in the safety of their children are recommended. © 2014 by Association of Pediatric Hematology/Oncology Nurses.
Kader I.A.,Islamic University of Lebanon
AIP Conference Proceedings | Year: 2013
In this paper, we analyze the bounds of path number in a directed graph, especially in the tournament Tn=(X,U), where we prove that: P(T n)≤n24-2 and from this remarkable result, we give some subclasses of tournaments for which we have P(Tn)=e(T n)=n24-2 (i.e. lower bound of P(G)=upper bound of P(G)) especially if An=(X,U) is the tournament having exactly m-n+1 elementary circuits we have: P(An)=e(An)=n24-2. Also we give a general theorem concerning the classes of directed graphs satisfying P(G)=e(G). The result obtained: 1. A procedure that allows a directed graph of order n satisfying P(G)=e(G), another class of directed graph G1 of order n+1 such that P(G1)=e(G1). 2. Generalized certain results proved by Alspach and Ore concerning the path partition number in directed graphs. © 2013 AIP Publishing LLC.
Barakat M.,University of Le Havre |
Barakat M.,Lebanese University |
Lefebvre D.,University of Le Havre |
Khalil M.,Lebanese University |
And 2 more authors.
International Journal of Machine Learning and Cybernetics | Year: 2013
Neural networks have been widely used in the field of intelligent information processing such as classification, clustering, prediction, and recognition. In this paper, a non-parametric supervised classifier based on neural networks is proposed for diagnosis issues. A parameter selection with self adaptive growing neural network (SAGNN) is developed for automatic fault detection and diagnosis in industrial environments. The growing and adaptive skill of SAGNN allows it to change its size and structure according to the training data. An advanced parameter selection criterion is embedded in SAGNN algorithm based on the computed performance rate of training samples. This approach (1) improves classification results in comparison to recent works, (2) achieves more optimization at both stages preprocessing and classification stage, (3) facilitates data visualization and data understanding, (4) reduces the measurement and storage requirements and (5) reduces training and time consumption. In growing stage, neurons are added to hidden subspaces of SAGNN while its competitive learning is an adaptive process in which neurons become more sensitive to different input patterns. The proposed classifier is applied to classify experimental machinery faults of rotary elements and to detect and diagnose disturbances in chemical plant. Classification results are analyzed, explained and compared with various non-parametric supervised neural networks that have been widely investigated for fault diagnosis. © 2012 Springer-Verlag.