A novel hybrid face mask detection approach using Transformer and convolutional neural network models.

Face and face mask detection are one of the most popular topics in computer vision literature. Face mask detection refers to the detection of people's faces in digital images and determining whether they are wearing a face mask. It can be of great benefit in different domains by ensuring public safety through the monitoring of face masks. Current research details a range of proposed face mask detection models, but most of them are mainly based on convolutional neural network models. These models have some drawbacks, such as their not being robust enough for low quality images and their being unable to capture long-range dependencies. These shortcomings can be overcome using transformer neural networks. Transformer is a type of deep learning that is based on the self-attention mechanism, and its strong capabilities have attracted the attention of computer vision researchers who apply this advanced neural network architecture to visual data as it can handle long-range dependencies between input sequence elements. In this study, we developed an automatic hybrid face mask detection model that is a combination of a transformer neural network and a convolutional neural network models which can be used to detect and determine whether people are wearing face masks. The proposed hybrid model's performance was evaluated and compared to other state-of-the-art face mask detection models, and the experimental results proved the proposed model's ability to achieve a highest average precision of 89.4% with an execution time of 2.8 s. Thus, the proposed hybrid model is fit for a practical, real-time trial and can contribute towards public healthcare in terms of infectious disease control.

Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain.

Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model's performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods.

Augmented Analytics Driven by AI: A Digital Transformation beyond Business Intelligence.

Lately, Augmented Analytics (AA) has increasingly been introduced as a tool for transforming data into valuable insights for decision-making, and it has gained attention as one of the most advanced methods to facilitate modern analytics for different types of users. AA can be defined as a combination of Business Intelligence (BI) and the advanced features of Artificial Intelligence (AI). With the massive growth in data diversity, the traditional approach to BI has become less useful and requires additional work to obtain timely results. However, the power of AA that uses AI can be leveraged in BI platforms with the use of Machine Learning (ML) and natural language comprehension to automate the cycle of business analytics. Despite the various benefits for businesses and end users in converting from BI to AA, research on this trend has been limited. This study presents a comparison of the capabilities of the traditional BI and its augmented version in the business analytics cycle. Our findings show that AA enhances analysis, reduces time, and supports data preparation, visualization, modelling, and generation of insights. However, AI-driven analytics cannot fully replace human decision-making, as most business problems cannot be solved purely by machines. Human interaction and perspectives are essential, and decision-makers still play an important role in sharing and operationalizing findings.

Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits.

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.