Classification of Sentiment Using Optimized Hybrid Deep Learning Model

Sentiment classification plays a pivotal role in natural language processing (NLP), and prior research has established the efficacy of utilizing convolutional neural networks (CNNs) and long short-term memory (LSTM) in this task. However, these approaches suffer from individual performance limitations: CNNs are limited to extracting local information and fail to express context information adequately, while LSTM networks excel at extracting context dependencies but exhibit long training times. To address this issue, we propose a novel text classification algorithm based on a hybrid CNN-LSTM model that leverages the strengths of both approaches and overcomes their limitations by combining them. Our approach is evaluated on the IMDB dataset, and we present a hyperparameter optimization framework utilizing Random Search to increase the likelihood of producing an optimally performing model

A Game theoretic approach for competition over visibility in social networks

Social Networks have known an important evolution in the last few years. These structures, made up of individuals who are tied by one or more specific types of interdependency, constitute the window for members to express their opinions and thoughts by sending posts to their own walls or others' timelines. Actually, when a content arrives, it's located on the top of the timeline pushing away older messages. This situation causes a permanent competition over visibility among subscribers who jump on opponents to promote conflict. Our study presents this competition as a non-cooperative game; each source has to choose frequencies which assure its visibility. We model it, exploring the theory of concave games, to reach a situation of equilibrium; a situation where no player has the ultimate ability to deviate from its current strategy. We formulate the named game, then we analyze it and prove that there is exactly one Nash equilibrium which is the convergence of all players' best responses. We finally provide some numerical results, taking into consideration a system of two sources with a specific frequency space, and analyze the effect of different parameters on sources' visibility on the walls of social networks

Face Detection in a Mixed-Subject Document

Before you recognize anyone, it is essential to identify various characteristics variations from one person to another. among of this characteristics, we have those relating to the face. Nowadays the detection of skin regions in an image has become an important research topic for the location of a face in the image. In this research study, unlike previous research studies  related  to  this  topic  which  have  focused  on  images  inputs  data  faces,  we  are  more interested to the fields face detection in mixed-subject documents (text + images). The face detection system developed is based on the hybrid method to distinguish two categories of objects from the mixed document. The first category is all that is text or images containing figures having no skin color, and the second category is any figure with the same color as the skin. In the second phase the detection system is based on Template Matching method to distinguish among the figures of the second category only those that contain faces to detect them. To validate this study, the system developed is tested on the various documents which including text and image

A game theoretic framework for controlling the behavior of a content seeking to be popular on social networking sites

Over the years, people are becoming more dependent on Online Social Networks, through whom they constitute various sorts of relationships. Furthermore, such areas present spaces of interaction among users; they send more messages and posts showing domains they are interested in to guarantee the level of their popularity. This popularity depends on its own rate, the number of comments the posted topic gets but; also on the cost a user has to pay to accomplish his task on this network. However, the selfish behavior of those subscribers is the root cause of competition over popularity among those users. In this paper, we aim to control the behavior of a social networks users who try their best to increase their popularity in a competitive manner. We formulate this competition as a non-cooperative game. We porpose an efficient game theoretical model to solve this competition and find a situation of equilibrium for the said game

Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

Search engines are now the main source for information retrieval due to the huge expansion of data on the internet over the last ten years. Providing users with the most relevant results for their queries poses a significant challenge for search engines. Semantic search engines, which go beyond traditional keyword-based searches, have appeared as advanced information retrieval systems to address this problem. These search engines produce more precise and pertinent search results because they understand the meanings of words and their relationships. They play a pivotal role in managing the vast amount of internet data, with a primary aim of enhancing search precision and user satisfaction. However, improving search precision remains as an important goal for natural language processing researchers. The main objective of our research is to improve the search engine results. We present a novel approach for measuring the similarity between a user’s query and a list of documents within a search engine. This approach provides a new fuzzy recommendation system using a syntactic and semantic similarity. Our results indicate that our method outperforms several existing approaches from the literature, achieving a high level of accuracy

Analysis of Competition Fronting the Popularity of Content in Social Networks

In the telecommunications domain they are several providers, but customers seeking those that there are good services. In this paper, a study is seeking on two types of providers: content providers CPs and Internet Service Providers ISPs. In this study, we analyzed the impact of Selfishness of Content Providers and Internet Service Providers on their strategies of Price and QoS on their decision strategies. Yet, we formulate our problem as a non-cooperative game among multiple CPs, multiple ISPs competing for the same market. We prove through a detailed analysis uniqueness of pure Nash Equilibrium (NE). Furthermore, a fully distributed algorithm to converge to the NE point is presented. In order to quantify how efficient is the NE point, a detailed analysis of the Price of Anarchy (PoA) is adopted to ensure the performance of the system at equilibrium. Finally, we provide an extensive numerical study to point out the importance of QoS and credibility in the market and the in-fluence of the existing economic relationship between content providers and Internet service providers

Recognition of a Face in a Mixed Document

Face recognition is the field of great interest in the domaine of research for several applications such as biometry identification, surveillance, and human-machine interaction…This paper exposes a system of face recognition. This system exploits an image document text embedding a color human face image. Initially, the system, in its phase of extraction, exploitis the horizontal and vertical histogram of the document, detects the image which contains the human face. The second task of the system consists of detecting the included face in other to determine, with the help of invariants moments, the characteristics of the face. The third and last task of the system is to determine, via the same invariants moments, the characteristics of each face stored in a database in order to compare them by means of a classification tool (Neural Networks and K nearest neighbors) with the one determined in the second task for the purpose of taking the decision of identification in that database, of the most similar face to the one detected in the input image

Vulnérabilité à la sécheresse des agrosystèmes du Sahel central : une approche de modélisation basée sur l'ampleur des changements et des techniques d'apprentissage automatique

International audienceAgricultural drought is a complex phenomenon with numerous consequences and negative implications for agriculture and food systems. The Sahel is frequently affected by severe droughts, leading to significant losses in agricultural yields. Consequently, assessing vulnerability to agricultural drought is essential for strengthening early warning systems. The aim of this study is to develop a new multivariate agricultural drought vulnerability index (MADVI) that combines static and dynamic factors extracted from satellite data. First, pixel temporal regression from 1981 to 2021 was applied to climatic and biophysical covariates to determine the gradients of trend magnitudes. Second, principal component analysis was applied to groups of factors that indicate the same type of vulnerability to configure the basic equation of vulnerability to agricultural drought. Then, random forest (RF), K-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) were used to predict drought vulnerability classes using the 28 factors as inputs and 708 pts of randomly distributed class labels. The results showed statistical agreement between the predicted MADVI spatial variability and the reference model (R=0.86 for RF) and its statistical relationships with the vulnerability subcomponents, with an R=0.73 with exposure to climate risk, R=0.64 with the socioeconomic sensitivity index, R=0.6 with the biophysical sensitivity index and a relatively weak correlation (R=0.21) with the physiographic sensitivity index. The overall vulnerability situation in the watershed is 21.8% extreme, 10% very high, 16.8% high, 27.7% moderate, 22.2% low and 1.5% relatively low considering the cartographic results of the predicted vulnerability classes with SVM having the best performance (accuracy=0.96, Kappa=0.95). The study is the first approach that uses the gradients of magnitudes of satellite covariate anomaly trends in multivariate modelling of vulnerability to agricultural drought. It can be easily scaled up across the Sahel region to improve early warning measures related to the impacts of agricultural drought