Advanced Information Technology, Services and Systems

The ageing population is considered to be a global challenge because of the reduction in fertility and the increase of life expectancy. In Saudi Arabia, the ageing population continues to age (>60 years of age) currently (5%) compared to other age groups. In 2050, it will rise rapidly to 20.9% of the Saudi population. Ambient Assisted Living (AAL) technology plays an important role in assisting elderly people to live in their home independently, longer, and improve their quality of life and health and in supporting their daily activities etc. The current research aims at examining the barriers that healthcare providers in the Kingdom of Saudi Arabia are experiencing in the adoption of AAL technologies among the elderly. The study identified some of the challenging issues with the increasing number of elderly people among the population in the country, which has highlighted the need to use AAL technologies to improve the quality of life among the elderly. The research involved a Community of Practice (CoP) study as a method of data collection where data collected was presented and discussed in line with the existing literature review findings. A lack of training, the high cost of AAL devices and the associated Management Information Decision Control System and cultural barriers were the main challenges identified in the research. The research suggests that awareness is important to encourage the elderly to accept the new technology and its potential in improving their quality of life. Training on the usage of these AAL devices should also be offered to ensure that self-care services are encouraged among the elderly who, in some cases, live away from their relatives.

Good management information system (IS) is the key of success of good IT governance and this can only be achieved if all strategic goals of the company are respected. That’s why, it will be necessary to ensure alignment with general objectives, to respond to the business model of the company and to define and anticipate the orientations of technical and economic choices.In this context, strategic alignment is applied to two elements: Business strategy and IT strategy and allows them to be linked [1]. However academics are more interested about the part concerning the strategic alignment [2, 3] and neglect the ideal conditions of its operation and continuous improvement.This works presents a model gathering between the concepts of strategic alignment presented by the Strategic alignment model (SAM) and principles of lean management presented by PDCA method.

Low-density parity-check (LDPC) codes and convolutional Turbo codes are two of the most powerful error correcting codes that are widely used in modern communication systems. This paper provides an overview of the basic concepts employed in LDPC codes and convolutional Turbo codes with 3 dimensions, and compare the performance of these codes. A description of both classes of codes will be given. The LDPC codes and 3 dimensional turbo code are coupled with receive diversity techniques and are employed as the error correction scheme over Additive White Gaussian Channels (AWGN) by employing Binary Phase Shift Keying (BPSK) modulation scheme. The performance of newly obtained codes is evaluated in term of bit error rate (BER) for a given value of Eb/No.

Smart buildings represent a prototypical cyber-physical system with deeply coupled embedded sensing and networked information processing that has increasingly become part of our daily lives. The notion of cyber physical is very important because the cyber word collects information from the various physical parameters through sensors and smart devices and on the basis of this information the main controller makes decisions and an energy distribution plan during outage period. Through context-aware sensing and computation the cyber-physical systems will be able to acquire contextual awareness information from the physical world and use this information in an intelligent way to build an effective energy management system based on smart distribution of power produced from renewable resources. The proposed approach allow a dynamic selection based on real time consumption measurement and information collected by different controllers installed in different entities inside building to decide the entities that will be powered during outage, the idea presents a way for optimization of restored energy and a intelligent strategy to response to customer demands based on their classification and their needing priority. An algorithm is developed to implement our approach with a simulation in Java environment to validate our efficiency management system through on-site distributed generators within buildings to operate in islanding mode. Finally, The objective of this paper is to propose a solution for new building generation that represent future smart cities concept and to have an self-supply based on distributed generation without needing to be connected to the main power grid.

This paper deals with the reverse flow management. We address the dynamic assignment problem of reusable containers in the supply chain (e.g. gas bottles, pallets, maritime containers, etc.). The objective is to optimize the collect, reloading, storage and redistribution operations taking into account the environmental constraints. We propose a newly generic mathematical model, which describes the studied problem. This mathematical model is solved following IBM ILOG CPLEX platform; although this method gives exact solutions, it is very time consuming, therefore, we adapted a hybrid approach based on a genetic algorithm to solve the problem at a reduced time. The numerical results show that the developed hybrid approach gives near optimal solutions in a moderate time.

The management of hospitals is characterized by a high degree of diversity and daily complexity of the various healthcare activities, reflecting both resource constraints and patient satisfaction. In this article we discuss the role of resource allocation modeling in hospitals, as the scheduling of its staff become troublesome, because of several elements that it builds upon, and this end up depleting a lot of time and resources. Our objective is to propose a multi-agent approach through scenarios while taking into account the various constraints related to competence and medical emergency. The simulation of the planning process dynamics and allocation of these resources is developed under the Netlogo platform.Finally, the approach is illustrated by examples inspired by actual cases experienced in a public health hospital.

The Internet of things (IoT) is a technical concretization of ubiquitous computing where technology is naturally integrated into the things of our daily life. The main objective of IoT is to make the world easier for human beings where things around us predict our preferences and context, and act autonomously without human interactions. Adapting multimedia documents to user context is a serious issue for user-aware development. Good context based-adaptive approach requires dynamic adaptation to the user, not only to its position, time of day, or to environment characteristics but also to its mood, preferences, or even disabilities. In this paper, we introduce a context-based approach which combines the IoT with semantic web services to predict the user current context and enable a dynamic adaptation of multimedia documents.

Recent studies discovered that the human brain has a deep face-processing network, where identity are processed by multiple different neurons. Consequently, we turn our attention for using deep architecture of neural networks to reach near human performance in the world of face recognition. In this paper, we make the following contributions: Firstly, we build a novel dataset with over four million faces labelled for identity by employing a smart synthesis augmented approach based on rendering pipeline to increase the pose and lighting variability. Secondly, a robust deep CNN model taking place. Finally, we set up a new real time application of this approach proposed. This application called PubFace, which allows users to identify anyone in public spaces. Experiments conducting on the well-known LFW dataset, demonstrating that the proposed approach achieved state-of-the-art results.

Face verification in unconstrained images, remains a challenging problem. Many works have been proposed to solve this problem. However, the performance gap existing between the human visual system and machines in face recognition remain important. This paper makes two contributions: firstly, for improving face recognition in the wild, at least in terms of pose variations, we propose a method for aligning faces by employing single-3D face model as reference produced by FaceGen Modeller. Secondly, we developed a novel face representation technique based on Gabor Filters. The proposed approach relies on combination of Gabor magnitude and Gabor phase informations into an unified framework, which capable to surpass standard representations in the well-known FERET dataset.

In this paper, we propose a novel descriptor for texture representation in Content-Based Image Retrieval (CBIR). Indeed, Gabor filters are the most used methods in this representation, but their major disadvantages are the choice of values and number of frequencies and orientations. To remedy this problem, our new method consists in extracting from the image itself with good precision the frequencies and the orientations; using the 2-D ESPRIT method (Estimation of Signal Parameters via Rotational Invariant Techniques); that will be injected in the Gabor filters. Our approach has been applied on the Coil_100 database, and the obtained results show the effectiveness and the rapidity of our technique compared to Gabor filters.

A retirement is on average 40 years of contributions and 25 years of pension: a lifetime legacy, a personal right that deserves to be given importance. The inability of current schemes to finance tomorrow’s pensions, reforms, and the complexity of careers … all these reasons motivated us to shed light on the processes of managing retirement. Therefore, we studied a specific case “The Moroccan pension scheme”, and we approached it from a Supply chain point of view, with the objective of studying and simulating this supply chain, which has as major challenge “ensuring the continuity between the salary and the pension”.

Business Intelligence (BI) systems have been adopted for decades to collect and analyze (periodically) a mass of relevant information from internal data sources. With the emergence of the Semantic Web (SW) technologies and vocabularies, no one could deny the necessity of including these external web sources in the decision-making process. However, the actual architecture of BI remains operational only in a well-controlled context where the sources are static and where the multidimensional scheme is defined in advance. Therefore, there is a strong need for new methods in order to extract information from dynamic data sources and enabling On-Line Analytical Processing (OLAP). In this paper, we propose a transposition method of multidimensional concepts over multiple ontologies sources in order to create the correspondent schema.

Since the advent of globalization and the evolution of organizations, the need for new and efficient processes for supply chain has become urgently important. One of supply chain management problems is the supplier selection which attracts the attention of many researches. Various efforts are made in this context, mainly the development of agent-based systems to automate the process of selecting suppliers. Negotiation is a critical approach to solve conflicting transaction between nodes and scheduling problems among supply chain members. In this paper, we propose a negotiation model based on agents to settle the problem of selecting supplier and transportation provider, and then alleviating the human interactions. The negotiation knowledge utilized by agent is organized by ontology in this paper, agents communicate via message exchange in the form of common ontology for agents participating in the negotiations.

This work aims to compare the learning features with Convolutional Neural Networks (CNN) and the handcrafted features. In order to determine which the best between these two type of features. We consider our previous baseline HMM system [1] for Arabic handwritten word recognition. Experiments have been conducted on the well-known IFN/ENIT database. Achieved results using CNN features are better than those obtained by the hand-crafted features. This demonstrates the high efficiency of CNN results from the strong capability for hierarchical feature learning given a large amount of data. However, Hand-engineered features are not generated from an optimization process to be compatible with the specific problem, and insufficient to be encoded with supervision.

Web services discovery occupies a crucial part in the semantic web, as it aims to return the most relevant web services that better meet the user’s needs. In this paper, we propose a new client-side web services architecture, designed to improve the performance of web services discovery. It is based on a hybrid approach that includes both the semantic and conceptual approach, which we called SCH-WSD (Semantic-Conceptual Hybrid approach for Web Services Discovery). SCH-WSD measures the degree of similarity between the queries and the web services using the inputs, outputs and category as matched elements. Ontology Web Language for Services (OWL-S) is used as semantic web services description language. A theoretical analysis and an experimental evaluation on some benchmarks illustrate the practical effectiveness of our approach and its ability to provide users with web services that perfectly meet their requirements.

With the existence of several energetic resources and local production site by consumers a new strategy for managing the distribution of energy is indispensable. This paper aims to develop a simulation platform for energy resources management of a Micro Grids Network to optimize the electricity consumption. Using the remote control systems and data integration from distributed databases the system regulates automatically the distribution following the need of each customer and need of Micro Grid. The solution use an incremental search algorithm based on the total satisfaction of the constraints by priority order. In this paper, as software platform solution, we use the multi-agent system (MAS) technology. This choice is motivated by the functional ability of agents, and their self-adaptation to the environment (i.e. change the feature). The ability of the interaction between the agents and their mobility will define and specify the real-time needs of each Micro Grids according to its production and consumption capacity and the need of its neighbors. The functional architecture of the operating system is based on a graph, where each node can be a customer or producer of energy or both of them associated with list of requirement constraints. We used the principle of Distributed Databases to facilitate communication inter-agents and to optimize the time of data transfer between agents of different Micro Grids and simplified access “on demand” to the data with high availability. Thanks to the distributed databases solution, we can easily integrate the critical data on a data center and improve the response time of readjustment and equilibration of the electricity distribution and consumption.

Today’s world is digital with the appearance of many devices that are used in image acquisition. Nowadays, it becomes easy to store huge amount of images by using image processing techniques. The rapid access to these masses collections of images and retrieve similar images of a given image (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For our purpose, we introduce in the content based image retrieval (CBIR) system the classification step, and we apply k-means clustering technique to match image’s descriptors. This work provides a detailed view of the solution we have adopted, and that perfectly meets our needs. For validation, we apply all of these techniques on two image databases in order to evaluate the performance of our system.

Enterprises in the 21th Century, with their numerous different activities, have a vision for their brand image to be recognized, for their human resources to be innovative, for their investments to be fruitful, to their Data to be meaningful, to their predictions to be right and to their Decisions to be productive. All those expectations and more; made and set by the Management Board are what designs their purpose & effectiveness. However this can’t be true in the Global Operational trending we are living in without having certain outlines to follow. Based on all the angles we pictured the workflow from, we organized and structured the Enterprise’s services and we made a call for the IT as a tool to handle all the aspect and also a number of IT frameworks such as ITIL, COBIT, CMMI that will help do the follow up. This is going to be the subject of our article in which we try to project the essence of Governance in Enterprises context.

Whether online or in a classroom, learners have long suffered from a lack of interactivity, several mobile learning system provide recorded lectures, which only enhance passive learning. Thus and in order to improve interactivity and motivation into the learning process, we propose in this paper a conceptual framework to be efficiently integrated into the learning process. This framework describes the design requirements of a mobile learning environment that can deliver live broadcast of real-time classroom teaching to online student with mobile devices. It suggests five perspectives: generic mobile environment, mobile learning context, learning experience, social learning strategy and learning objectives. It also clarifies how to apply these last perspectives and build a mobile learning application efficiently.

A complex concept that many structures tend to avoid and neglect, this is an opportunity to have an insight of the concept of Knowledge Management plus get advantage of learning about it from a perspective of different disciplines in Science, Their combination gives the recipe for a right and so called complete frame of Knowledge Management. In this paper we give an overview on the state of art in knowledge management that can handle governance of the business afterwards without having to worry about the wellness of the internal environment and how to capture it.

The requirements of cloud computing are putting the traditional networks in tension which influence the quality of the services provided by cloud computing. Therefore, the application of software defined-network (SDN) within cloud computing reinforces the dynamicity and flexibility of cloud. Recently, SDN is the trend in networking and virtualized networks, where, SDN separate the network control plane from the data plane, which leads the management of the network routing from decentered architecture to centered architecture. Despite the advantages of merging the SDN paradigm within the cloud environment, the security issues still in the surface. This paper presents a survey on the security issues in software-defined networking and the challenges faced by admins and providers in order to guarantee a secure environment with a resume about the proposed solution.

Authentication is the most security service required by Session Initiation Protocol (SIP). In recently years, Zhang et al. proposed for the first time an efficient and flexible authentication protocol for SIP using smart card and Elliptic Curve Cryptography. But, in 2014, Zhang et al. showed that their latest proposed protocol is vulnerable to impersonation attack. In order to improve their protocol, Zhang et al. proposed a second protocol. However, in this work we demonstrate that Zhang et al.’s protocol is vulnerable to server spoofing attack. Furthermore to overcome the weakness of Zhang et al.’s protocol we propose an improved and secured SIP authentication and key exchange protocol. The security analysis shows that our proposed protocol can resist to various attack including server spoofing attack.

To maintain the proper functioning of critical applications based on Wireless Sensor Networks, we must provide an acceptable level of security while taking into account limited capabilities of the sensors. In this paper we propose a mobile approach to secure data exchanged by structured nodes in cluster. The approach is based on mobile nodes with significant calculation and energy resources that allow cryptographic key management and periodic rekeying. However, mobility in wireless sensor networks aims to increase the security and lifetime of the entire network. The technical methods used in this paper are based on cryptography elliptic curves and key management through a balanced binary tree. To compare the performance of the proposed approach with other mobile algorithms, we focus on the following metrics: the energy consumed by normal sensors and cluster heads, the number of packets exchanged during key installation, time to generate and distribute cryptographic keys and the memory used by the different sensors to store keys.

Region outage is one of distribution system and most of time caused by weather or a fault in transmission lines. Smart grid is a new concept that can be a solution for few people existing in a far region to avoid islanded problem. Thanks to IT evolution and the integration of the renewable resources in the existing electrical grid and the bi-directional communication ensured in distribution network, we can distribute power smartly to response all demands or response the emergencies demands. In this paper we propose a potential outage management and a new approach of distribution based on some collected region parameters and based on the potential of distributed generation like solar panel, wind turbine and storage batteries. Islanding region can be detected with some existing islanding detection methods. Smart loads selection is done through an intelligent controller who collects information from different controllers and sensors. The regional data center analyzes and decides a layered tree that contains consumers who will be covered by the outage operation. An algorithm is developed to implement our approach with some distribution simulation to discuss and analyze results.

Mobile Ad Hoc network (MANET) is a collection of smart mobile nodes, which form a dynamic and autonomous system. These nodes communicate wirelessly in a self-organized, self-configured and self-administered manner. Routing protocol is the main building block in route establishment and traffic delivery, which must be accomplished anywhere and anytime, between a pair of source and destination. Therefore, research interest in MANETs has been growing, and particularly the design of MANET routing protocols has gained a lot of interest. Furthermore, constantly changing network topology, limited bandwidth and energy issues make the task of routing in MANETs a challenging one. In this paper we provide the taxonomy of routing protocols for MANETs, which constitute the main key behind the design of routing protocols process.

The massive diffusion of Cloud services in the internet led to increasing demands of services and cloud infrastructure are conduct to an increasing in energy consumption in data centers, which has presents interesting great challenge. In this paper, we propose new strategy of allocation resources, with intelligent management of resources our approach based on constraint programming (CP), according to the autonomous resources allocation using multi-agent systems (SMA). At first, we make a review of various solutions that have been proposed in order to optimize Cloud energy consumption; second, we offer a logical solution to manage physical and virtual resources in smarter data center. At the end, we conclude this paper by future work.

We propose an approach to provide the cryptography key management system (CKMS) as a trusted security service in Cloud Computing, based on the trusted platform module (TPM/vTPM). In this approach we have used the TPM’s capabilities/functions as a secure way and a root of trust for this kind of services. Therefore, and as an application case, we have used TPM’s key generation component as a trusted way to generate and to sign any encryption/signing keys by the CKMS for their customers.

Geographic Information Systems (GIS) plays an essential role in wide range of areas and is extensively adopted nowadays. They combine social, economic and topographical data that is used for a variety of purposes including flood defense planning, healthcare and road traffic management. GIS is a collection of tools that captures, stores, analyzes, manages, and presents data that are linked to geographical locations. This technology continues to change the way to manage infrastructure, emergency response, and planning [1, 2]. Several efforts are being made to upgrade the conventional GIS applications in order to improve decision making, deliver better service, and reduce operating costs. “Cloud computing” a term which has become popular in recent years, has appeared as technologies by its focus on large-scale asset sharing and reduced cost for large-scale data storage technology to be applied to solve and overcome the challenges in GIS applications [3]. Many efforts are being made to upgrade the GIS applications in order to improve decision making, deliver better service, and reduce operating costs. “Cloud computing” a term which has become popular in recent years, has appeared as technology able to solve and overcome the challenges in GIS applications, by its focus on large-scale asset sharing and reduced cost for large-scale data storage. In this paper, a brief evaluation of Cloud Computing approach to GIS is presented and architecture for GIS Cloud System is proposed.

In order to help doctors to communicate in real time with patients, to better diagnose their problems and protect the confidentiality of medical information. We propose a new real-time cloud telemedicine based on voice over IP protocol (VoIP). A prototype based on elliptic curve and digital signature has been developed to confirm a secure encryption scheme.

Data access control is of critical importance in cloud computing, in particular for e-health systems, where a patient Personal Health Records (PHR) data, have a serious privacy concerns about outsourcing to the cloud servers. Presently Key policy attribute based encryption (KP-ABE) is promising advanced cryptographic system for fine-grained access control in cloud computing systems. Yet, Existing access control schemes based on attribute based encryption (ABE), are no longer applicable due to the heavy cryptographic computation and communication overhead of key management. Existing ABE schemes are based on expensive bilinear pairing that make its not scalable and not suitable for cloud e-health systems. In this paper we propose a new Scalable lightweight LKP-ABE scheme based on elliptic curve integrated encryption scheme (ECIES), The best known encryption scheme based on Elliptic Curve Cryptography, applied in e-health system, in order to ensure fine grained access control and data confidentiality of personal health records, and present an advanced secure and scalable encryption/decryption system based on Key Policy Attribute Based Encryption (KP-ABE) for PHR’s. our scheme provide semantic security against chosen cipher-text attacks (CCAs), guaranteed resistance collusion and provide hight level data confidentiality of sharing PHR, by using elliptic curve integrated encryption scheme (ECIES) that has much stronger bit security than RSA as well as other exponential-based public key algorithm and the advanced attribute based encryption KP-ABE. The proof security, performance comparison among LKP-ABE and related schemes is given to prove the performance, low cost communication and execution efficiency of LKP-ABE.

In this paper, we conduct a comparative study about the impact of stemming algorithms, as feature extraction systems, on the task of classification of Arabic text documents. Stemming is forceful and fierce as in reducing words to their three-letters roots. Which may influence the semantics, as various words with divers implications may share the same root. Light stemming, by examination, expels oftentimes utilized prefixes and suffixes in Arabic words. Light stemming doesn’t extract the root and thus doesn’t influence the semantics of words. However, the result of the light stemming is not necessarily a word. For the evaluation, we used corpus contains 5,070 records that fall into six classes. A several tests were done utilizing two separate illustrations of the same corpus. The K-Nearest Neighbors (KNN) classifier was utilized for the classification task. The recall measure is used to evaluate the performance of these methods.

Data mining is about extracting useful knowledge from data. It has various techniques and algorithms. Yet, the most widely used are classification algorithms which deal with the problem of affecting new data element to one of predefined classes. There are a wide range of classification algorithms such as decision trees, neural networks, K-NN, Bayes, support vector machines (SVM); and so on. This study focuses on four algorithms; Naive Bayes, Multi-Layer Perceptron (MLP), SVM and C4.5; all of them are based on mathematical calculations but in different ways. In this paper, we aim to make a comparison between the four algorithms in terms of well-chosen criteria like classification accuracy and execution time. Moreover, we implement these algorithms with the same dataset; relative to diabetes on women; in order to present the different results by using Waikato environment for knowledge analysis (Weka).

No one can deny the emergence of semantic web technologies with their considerable performance in data management offering a better cooperation between people and computers, but unfortunately, relational databases are still the most used; therefore establishing a connection between them becomes an active topic aiming to bridge the gap between the both heterogeneous systems. Regarding the interoperability between their query languages, more precisely, SQL-to-SPARQL query transformation direction, some solutions have been explored to ensure this conversion, but all these approaches have the same gap because they start the mapping process before analyzing and optimizing the input SQL query; this weakness has motivated us to add a pretreatment phase aiming to optimize some SQL components with a specific focus on Left and Right Outer Join command(s) generating the most complex component SPARQL query (Optional pattern(s)) in order to reduce complexity of the output SPARQL query.

Relational Databases are currently at the heart of information system of the companies. In recent years, the relational model has become de facto standard thanks to its maturity and efficiency. However, the fact that the data of some companies or institutions have become too large, new systems has appeared namely NoSQL which belongs to the Big Data era. Big Data comes due to the emergence of new online services on which customers have become increasingly connected, which creates a large digital data unbearable by the traditional management technical tools, which raise new challenges for companies especially to access, store and analyse data. In this paper we will propose a feasibility study of migration from relational databases to NoSQL databases specifically HBase database, by applying the operations of the relational algebra in HBase data model and explore the implementation of these operations on HBase by using the native functions of this DBMS and also by using the MapReduce Framework.

Banking industry is one of the most complex and sensitive industries that experience enormous changes in daily basis. Likemany others businesses, Big data is a serious problematic, data management and real time monitoring fraud issues also are even bigger challenges in this sector, due to the huge quantity of data, coming swiftly and rapidly from different devices in structured and unstructured formats, waiting for instantaneously treatments and decisions. Most financial institutions and banks try to innovate and diversify payment processes to make it more challenging and secure to improve their digital skills. Understand customer’s behaviors also become a successful key factor in the market at the same time, that’s why Internet of Things (IoT) can be the best solution to solve the issue of collecting and sharing data via internet among different “things”, as devices and objects (Sensors, ATMs, POS, Smartphones, Computers, payment gateways (ecommerce), notebooks, etc.). The architectural and technical sides remain a problem, since conventional database management system and existing banking systems are not capable anymore to handle, store and process this massive volume of data with sufficient real time. This paper, discuss Hadoop Distributed File System and MapReduce, as an architecture for storing and retrieving information from massive volumes of datasets that we can collect via Internet from different objects based on the advantage and potential of Internet of things.

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, search, sharing, storage, transfer, visualization, querying, and updating and information privacy. However, these huge data cannot easily handle since the most of CS systems are relational, and an adjustment is needed before any processing. With emergence of Big Data, new NoSQL systems come to deal with this relational data issue. So, we propose an approach to migrate historical CS data from relational to NoSQL system, and use a distributed environment containing many nodes. As experimentation, we migrate data generated by an oil and gas CS to an appropriate distributed NoSQL system, and we perform some data mining experiments on them in order to compare results and prove the obtained performance.

Because they’re facilitating life, using computers and other intelligent devices associated with internet has become vital in those days. Banking transactions, education, trade marketing, texting … are all daily and important operations that relies on such technology. Information systems that handle those operations must be kept secure from any intrusive activity. To help ensure that, we must take into consideration several subjects such as access control by managing confidentiality, integrity and availability, as well as deploying detection and prevention tools and mechanisms that help preparing for and dealing with attacks. In this perspective, we propose a network intrusion detection model based on multiclass logistic regression (MLR) and Correlation-based feature selection (CFS). Results will be discussed with respect to NSL-KDD Dataset, and compared to other techniques based on various classification methods.

The major evolution of the search engine is to understand the user’s query and the intention of the user. The other change in size and all of you is the evolution of the mobile query. Indeed, research on search engines is trying today to bring results of research adapted to the intention of the user. Understand the search intentions of Internet users and paramount to the search engines. But it is also for SEO. By understanding the user’s intention to search, we can define the type of content to produce in order to maximize your chances of positioning.In this article, we focus on detecting and understanding the user’s intent that motivates a user to search the web. The analysis of the history of research is the detailed examination of data on the Web different users for the purpose of understanding and optimizing web management. In this article we propose a new approach to detect user intent through search engine optimization to improve the ranking of a website in organic search results to increase visibility and quality.

Recognizing unconstrained cursive Arabic handwritten text is a very challenging task the use of hybrid classification to take advantage of the strong modeling of Hidden Markov Models (HMM) and the large capacity of discrimination related to Multilayer Perceptron (MLP) is a very important component in recognition systems. The proposed work reports an effective method on improvement our previous work that takes into consideration the context of character by applying an embedded training based HMMs this HMM is enhanced by an Artificial neural network that are incorporated into the process of classification to estimate the emission probabilities. The experiments are done on the same benchmark IFN/ENIT database of our previous work to compare the results and show the effectiveness of hybrid classifier for enhancing the recognition rate the results are promising and encouraging.

Nowadays, emergency department encounters several difficulties to provide quality service to patients, especially inpatient unit that faces a big number of patients random arrivals with different ages and acuities. Patients must be examined and treated in a restricted time, while the constraint of this unit which is limited capacity (human and materiel resources) given the big daily load creates high length of stay, long waiting times and then overcrowding. Those factors impact patient satisfaction and service quality. So, our goal is patients’ length of stay and waiting time reduction by increasing inpatient unit service rate according to care load. In this paper, we present our approach which consists essentially in determining the adequate combinations of human and materiel resources to be attributed to each inpatient unit room, in order to insure and provide the optimal service rate. This approach is performed using queuing theory.

This paper is to present a hybrid technique of two metaheuristic algorithm Penguins Search optimization Algorithm (PeSOA) and the genetic algorithm (GA) called HPeSOA, which was proposed to solve the combinatorial optimization problem NP-hard Traveling salesman problem. In this algorithm, we improve the population of the solutions by the integration of the genetic operators, namely the crossover and the mutation in the algorithm PeSOA. The experimental results of the application of HPeSOA algorithm on the instances TSPLIB are reported and compared, with the results of Penguins Search optimization Algorithm and the genetic algorithm.

Currently, pattern recognition is an attractive and popular field of research, and requires the emergence of new tools. Artificial intelligence techniques like neural networks are efficient candidates. Various neural algorithms have been employed to solve recognition tasks. In this paper, we focus on the use of Counter Propagation network “CPN” that, combines two modes of learning. A special attention is given to the linear regularities in learning data, and its impact on the success of the Counter-propagation. This work investigates the abilities of CPN for recognition, and detects the ambiguities encountered in the learning process. Experimental tests approve the influence of this problem on the exactitude of the recognition during its application, and allow suggesting an optimization procedure called the Principal components analysis in order to eliminate the detected problems, and to increase the accuracy of the algorithm.

This paper presents an approach for migrating existing temporal relational database (TRDB), into temporal object relational database (TORDB). This is done by enhancing a representation of a varying time database’s structure, in order to make hidden semantic explicit. In contrast to other studies, our main goal here is to offer a first and better solution to mentioned limits to existing works, in order to provide the efficient and the correct method for the translation from TRDB to TORDB. We are going to take an existing RDB using valid time features as input, enrich its metadata representation, and generate a new valid time data Model (NVTM), which captures the most important characteristics of temporal databases for conversion. From the NVTM, we will develop our TORDB design scheme in order to simplify the implementation of a temporal object. Through this UML profile, we precede to the last step, the creation of temporal object relational tables integrating valid time aspects.

The technique of binary data sequence generator based on LFSR is used a variety of cryptographic applications and for designing an encoder/decoder in different communication channel. It is more important to test and verify by implementing on any hardware device to get a better effective result. As FPGA is used to implement any logical function for faster prototype development, it is necessary to implement the existing LFSR design on FPGA to test and verify the result of simulation and synthesis between different lengths. The total number of random states generated on LFSR depends on the feedback polynomial. The binary data generator is implemented using an LFSR shift register. This is a 23-bit shift register. Random Number Generator allows you to generate a random number with the selected length. The maximum length is 223−1.

The challenges facing learning of reasoning require a general refounding of knowledge modeling expressed by the development and integration of intelligence and reasoning techniques in the processing of this knowledge. In this paper, we present a part of the research work of PERO2 project. This intelligent system is a machine teaching dedicated to the learning of reasoning and problem-solving of exercises in physical science. Our research is focused on representing the knowledge base of PERO2 by integrating a semantic layer based upon a domain ontology, capable of adding some intelligence and enriching the reasoning in our system. In order to build this ontology, we propose a hybrid method based on two main phases: (*) design phase of our domain ontology called “OntoPhyScEx”. (**) Semantic validation phase of this ontology.