Information Retrieval: A Comparative Study of Textual Indexing using an Oriented Object Database (DB4O) and the Inverted File

The Growth in the volume of text data such as books and articles in libraries for centuries has imposed to establish effective mechanisms to locate them. Early techniques such as abstraction, indexing and the use of classification categories have marked the birth of a new field of research called "Information Retrieval". Information Retrieval (IR) can be defined as the task of defining models and systems whose purpose is to facilitate access to a set of documents in electronic form (corpus) to allow a user to find the relevant ones for him, that is to say, the contents which matches with the information needs of the user. Most of the models of information retrieval use a specific data structure to index a corpus which is called "inverted file" or "reverse index". This inverted file collects information on all terms over the corpus documents specifying the identifiers of documents that contain the term in question, the frequency of each term in the documents of the corpus, the positions of the occurrences of the word. In this paper we use an oriented object database (db4o) instead of the inverted file, that is to say, instead to search a term in the inverted file, we will search it in the db4o database. The purpose of this work is to make a comparative study to see if the oriented object databases may be competing for the inverse index in terms of access speed and resource consumption using a large volume of data

A Contribution to Secure the Routing Protocol "Greedy Perimeter Stateless Routing" Using a Symmetric Signature-Based AES and MD5 Hash

This work presents a contribution to secure the routing protocol GPSR (Greedy Perimeter Stateless Routing) for vehicular ad hoc networks, we examine the possible attacks against GPSR and security solutions proposed by different research teams working on ad hoc network security. Then, we propose a solution to secure GPSR packet by adding a digital signature based on symmetric cryptography generated using the AES algorithm and the MD5 hash function more suited to a mobile environment

Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results

An Approach of Semantic Similarity Measure between Documents Based on Big Data

Semantic indexing and document similarity is an important information retrieval system problem in Big Data with broad applications. In this paper, we investigate MapReduce programming model as a specific framework for managing distributed processing in a large of amount documents. Then we study the state of the art of different approaches for computing the similarity of documents. Finally, we propose our approach of semantic similarity measures using WordNet as an external network semantic resource. For evaluation, we compare the proposed approach with other approaches previously presented by using our new MapReduce algorithm. Experimental results review that our proposed approach outperforms the state of the art ones on running time performance and increases the measurement of semantic similarity

Effect of Measurement Factors on Photovoltaic Cell Parameters Extracting

In this paper, we study the influence of external factors on the measurement for the current–voltage (I-V) characteristic of the photovoltaic cell. These factors are the size of the number of measurements, the range of the cell generated voltage and the influence of measures step and mode combination of photovoltaic cells (parallel, serial, or hybrid). The main extracted parameters solar cell are the photocurrent Iph, the reverse diode saturation current I0, the ideality factor of diode n, the series resistance Rs and the shunt resistance Rsh. A method for finding these parameters, according to the single-diode model, was developed by Newton-Raphson’s method using Matlab. To assess the accuracy of this method, measured and calculated I–V characteristics were compared with provided data by the manufacturer at standard test condition (STC). The measurement results showed that these parameters are highly dependent on these four factors

Contribution à la Sécurisation du protocole de routage ‘’Greedy Perimeter Stateless Routing ‘’ à l'aide de l'algorithme AES et du hachage MD5

Ce travail présente une contribution pour sécuriser le protocole de routage GPSR (Greedy Perimeter Stateless Routing ) pour les réseaux ad hoc véhiculaire, nous examinons dans un premier temps les attaques possibles contre GPSR et les solutions de sécurité proposées par les différentes équipes de recherche travaillant sur la sécurité des réseaux ad hoc. Ensuite, nous proposons une solution pour sécuriser GPSR en ajoutant au paquet GPSR une signature numérique basée sur la cryptographie symétrique générées à l’aide de l’algorithme AES et la fonction de hachage MD5 plus adapté à un environnement mobile

Information Retrieval: Textual Indexing Using an Oriented Object Database

The growth in the volume of text data such as books and articles in libraries for centuries has imposed to establish effective mechanisms to locate them. Early techniques such as abstraction, indexing and the use of classification categories have marked the birth of a new field of research called "Information Retrieval". Information Retrieval (IR) can be defined as the task of defining models and systems whose purpose is to facilitate access to a set of documents in electronic form (corpus) to allow a user to find the relevant ones for him, that is to say, the contents which matches with the information needs of the user.  Most of the models of information retrieval use a specific data structure to index a corpus which is called "inverted file" or "reverse index". This inverted file collects information on all terms over the corpus documents specifying the identifiers of documents that contain the term in question, the frequency of each term in the documents of the corpus, the positions of the occurrences of the word. In this paper we use an oriented object database (db4o) instead of the inverted file, that is to say, instead to search a term in the inverted file, we will search it in the db4o database. The purpose of this work is to make a comparative study to see if the oriented object databases may be competing for the inverse index in terms of access speed and resource consumption using a large volume of data

Using a Profiling System to Recommend Employees to Carry out a Project

In this paper, we introduce the application of a profiling system to suggest appropriate employee profiles for project assignments based on task specifications. The primary objective of this system is to assist managers in gaining a comprehensive understanding of their employees’ profiles and motivations. Our research introduces a recommendation system that relies on a profiling approach, analyzing messages and publications shared within a professional network. The proposed system is composed of two main components. The first component focuses on profiling, extracting relevant information from the company’s Human resources (HR) data, identifying interests, and establishing a psychological profile from publications exchanged within the professional platform. The second component is dedicated to recommending profiles that closely align with the specific requirements of each project. Our system yields promising results in predicting favored candidates for projects, achieving an accuracy of 0.92 and an F-score of 0.94. By integrating message-based profiling and leveraging data from professional networks, our approach proves to be effective in recommending well-suited candidates for various projects