An improved Arabic text classification method using word embedding

Feature selection (FS) is a widely used method for removing redundant or irrelevant features to improve classification accuracy and decrease the model’s computational cost. In this paper, we present an improved method (referred to hereafter as RARF) for Arabic text classification (ATC) that employs the term frequency-inverse document frequency (TF-IDF) and Word2Vec embedding technique to identify words that have a particular semantic relationship. In addition, we have compared our method with four benchmark FS methods namely principal component analysis (PCA), linear discriminant analysis (LDA), chi-square, and mutual information (MI). Support vector machine (SVM), k-nearest neighbors (K-NN), and naive Bayes (NB) are three machine learning based algorithms used in this work. Two different Arabic datasets are utilized to perform a comparative analysis of these algorithms. This paper also evaluates the efficiency of our method for ATC on the basis of performance metrics viz accuracy, precision, recall, and F-measure. Results revealed that the highest accuracy achieved for the SVM classifier applied to the Khaleej-2004 Arabic dataset with 94.75%, while the same classifier recorded an accuracy of 94.01% for the Watan-2004 Arabic dataset

Spatio-Temporal Analysis of the Remote Sensing Ecological Index – A Case Study of the Favorable Agro-Ecological Zone in Northwest Morocco

Agriculture has traditionally been one of Morocco's most important industries, providing the largest percentage of the nation's GDP (Gross Domestic Product). However, over the past two decades, the frequency and severity of Morocco's droughts have grown. These climate changes have a direct impact on essential crops in the country. Exploring the geographical and temporal evolution of the ecological quality is thus critical for the conservation of the natural environment. To achieve this, the present study attempted to evaluate seasonally the environmental quality in the most favorable agro-ecologic zone in Morocco, using remote sensing data, in the years 2001, 2011, and 2021. An index was created, called Remote Sensing Environmental Index (RSEI), which combines four ecological indicators, related to vegetation, humidity, heat, and dryness aspects. The results indicate that from 2011 to 2021, the RSEI values deteriorated the greatest, particularly during the winter months. In addition, vegetation and humidity were the parameters most affecting the RSEI index. Thus, the key drivers of the improvement in the environmental quality are the establishment of ecological policies, rules, and actions to maintain a sustainable environmental development

A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation.

We performed RNA sequencing on 40,000 cells to create a high-resolution single-cell gene expression atlas of developing human cortex, providing the first single-cell characterization of previously uncharacterized cell types, including human subplate neurons, comparisons with bulk tissue, and systematic analyses of technical factors. These data permit deconvolution of regulatory networks connecting regulatory elements and transcriptional drivers to single-cell gene expression programs, significantly extending our understanding of human neurogenesis, cortical evolution, and the cellular basis of neuropsychiatric disease. We tie cell-cycle progression with early cell fate decisions during neurogenesis, demonstrating that differentiation occurs on a transcriptomic continuum; rather than only expressing a few transcription factors that drive cell fates, differentiating cells express broad, mixed cell-type transcriptomes before telophase. By mapping neuropsychiatric disease genes to cell types, we implicate dysregulation of specific cell types in ASD, ID, and epilepsy. We developed CoDEx, an online portal to facilitate data access and browsing

A Single-Cell Transcriptomic Atlas of Human Neocortical Development during Mid-gestation.

We performed RNA sequencing on 40,000 cells to create a high-resolution single-cell gene expression atlas of developing human cortex, providing the first single-cell characterization of previously uncharacterized cell types, including human subplate neurons, comparisons with bulk tissue, and systematic analyses of technical factors. These data permit deconvolution of regulatory networks connecting regulatory elements and transcriptional drivers to single-cell gene expression programs, significantly extending our understanding of human neurogenesis, cortical evolution, and the cellular basis of neuropsychiatric disease. We tie cell-cycle progression with early cell fate decisions during neurogenesis, demonstrating that differentiation occurs on a transcriptomic continuum; rather than only expressing a few transcription factors that drive cell fates, differentiating cells express broad, mixed cell-type transcriptomes before telophase. By mapping neuropsychiatric disease genes to cell types, we implicate dysregulation of specific cell types in ASD, ID, and epilepsy. We developed CoDEx, an online portal to facilitate data access and browsing