Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing press articles and blog posts about a particular product or company, and it requires a high concentration, especially when the topic being discussed is sensitive. Nevertheless, most existing models and techniques are designed to process short text from social networks and collaborative platforms. In this paper, we propose a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) models, with Doc2vec embedding, suitable for opinion analysis in long texts. The CNN-BiLSTM model is compared with CNN, LSTM, BiLSTM and CNN-LSTM models with Word2vec/Doc2vec embeddings. The Doc2vec with CNN-BiLSTM model was applied on French newspapers articles and outperformed the other models with 90.66% accuracy.

Financial time series are volatile, non-stationary and non-linear data that are affected by external economic factors. There is several performant predictive approaches such as univariate ARIMA model and more recently Recurrent Neural Network. The accurate forecasting of budget data is a strategic and challenging task for an optimal management of resources, it requires the use of the most accurate model. We propose a predictive approach that uses and compares the Machine Learning ARIMA model and Deep Learning Recurrent LSTM model. The application and the comparative analysis shows that the LSTM model outperforms the ARIMA model, mainly thanks to the LSTMs ability to learn non-linear relationship from data.

As technology develops, consumer behavior and how people search for what they want are constantly evolving. Online shopping has fundamentally changed the e-commerce industry. Although there are more products available than ever before, only a small portion of them are noticed; as a result, a few items gain disproportionate attention. Recommender systems can help to increase the visibility of lesser-known products. Major technology businesses have adopted these technologies as essential offerings, resulting in better user experiences and more sales. As a result, recommender systems have achieved considerable economic, social, and global advancements. Companies are improving their algorithms with hybrid techniques that combine more recommendation methodologies as these systems are a major research focus. This review provides a thorough examination of several hybrid models by combining ideas from the current research and emphasizing their practical uses, strengths, and limits. The review identifies special problems and opportunities for designing and implementing hybrid recommender systems by focusing on the unique aspects of big data, notably volume, velocity, and variety. Adhering to the Cochrane Handbook and the principles developed by Kitchenham and Charters guarantees that the assessment process is transparent and high in quality. The current aim is to conduct a systematic review of several recent developments in the area of hybrid recommender systems. The study covers the state of the art of the relevant research over the last four years regarding four knowledge bases (ACM, Google Scholar, Scopus, and Springer), as well as all Web of Science articles regardless of their date of publication. This study employs ASReview, an open-source application that uses active learning to help academics filter literature efficiently. This study aims to assess the progress achieved in the field of hybrid recommender systems to identify frequently used recommender approaches, explore the technical context, highlight gaps in the existing research, and position our future research in relation to the current studies.

Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.

Background: Clinical imaging is an important part of health care providing physicians with great assistance in patients treatment. In fact, segmentation and grading of tumors can help doctors assess the severity of the cancer at an early stage and increase the chances of cure. Despite that Deep Learning for cancer diagnosis has achieved clinically acceptable accuracy, there still remains challenging tasks, especially in the context of insufficient labeled data and the subsequent need for expensive computational ressources. Objective: This paper presents a lightweight classification and segmentation deep learning model to assist in the identification of cancerous tumors with high accuracy despite the scarcity of medical data. Methods: We propose a multi-task architecture for classification and segmentation of cancerous tumors in the Brain, Skin, Prostate and lungs. The model is based on the UNet architecture with different pre-trained deep learning models (VGG 16 and MobileNetv2) as a backbone. The multi-task model is validated on relatively small datasets (slightly exceed 1200 images) that are diverse in terms of modalities (IRM, X-Ray, Dermoscopic and Digital Histopathology), number of classes, shapes, and sizes of cancer pathologies using the accuracy and dice coefficient as statistical metrics. Results: Experiments show that the multi-task approach improve the learning efficiency and the prediction accuracy for the segmentation and classification tasks, compared to training the individual models separately. The multi-task architecture reached a classification accuracy of 86%, 90%, 88%, and 87% respectively for Skin Lesion, Brain Tumor, Prostate Cancer and Pneumothorax. For the segmentation tasks we were able to achieve high precisions respectively 95%, 98% for the Skin Lesion and Brain Tumor segmentation and a 99% precise segmentation for both Prostate cancer and Pneumothorax. Proving that the multi-task solution is more efficient than single-task networks.

Digitalization, interconnection, open data, and the use of internet and social media by governments and citizens have consequently leaded to an enormous growth of data in the public sector. As government data available increases, many big data initiatives have been launched by governments in order to derive insights and create new value in many areas. This paper details some concepts related to government data specifically open data and big data in order to draw the relationships between them and show its potential value. The article also brings into light the impact of digital progress, made so far in the Moroccan Administration, on the growth of data in the public sector, and presents first steps taken by the government toward big data era. In particular, we focus on social media, open government data and e-government initiatives. In addition, the paper showcases examples of applying advanced analytics in the public finances specifically in Tax Administration to uncover insights and make better decisions from large datasets. This article also overviews some challenges facing future big data initiatives in the Moroccan public sector and proposes recommendations to address them.