Human Action Recognition (HAR) has been a prominent area of research within machine learning over the last few decades. Its applications span domains such as visual surveillance, robotics, and pedestrian detection. Despite the numerous techniques ...

The digitization of variety identification is of great importance for the improvement of farming practices in date fruit production. In this study, we have developed a hybrid approach called DenseViT-XGB for date fruit variety identification. ...

Federated learning (FL) is a decentralized approach to training machine learning model. In the traditional architecture, the training requires getting the whole data what causes a threat to the privacy of the sensitive data. FL was proposed to ...

The increasing prevalence of Internet of Things (IoT) systems has made them attractive targets for malicious actors. To address the evolving threats and the growing complexity of detection, there is a critical need to search for and develop new ...

In today’s interconnected world, the transmission of both lengthy and concise text messages is ubiquitous across diverse communication platforms. With the proliferation of sensitive and specialized information being exchanged, safeguarding these messages ...

In recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy. Researchers have ...

Communication through speech can be hindered by environmental noise, prompting the need for alternative methods such as lip reading, which bypasses auditory challenges. However, the accurate interpretation of lip movements is impeded by the uniqueness of ...

Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a ...

Automatic text classification, in which textual data is categorized into specified categories based on its content, is a classic issue in the science of Natural Language Processing (NLP). These models have proven useful when applied to data with ...

Identifying anomalous activity is a heavy task, and this has led to the progression in the domain of deep learning for video surveillance. With the development of deep learning, anomaly detection techniques have been widely used to improve the ...

To further improve the security of the image encryption methods based on chaotic maps, we created a new two-dimensional chaotic map called two Dimensional Beta Chaotic Map(2D-BCM) driven from the one-dimensional Beta chaotic map(1D-BCM). This ...

One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS ...

Sleep apnea is one of the most common sleep disorders in the world. It is a common problem for patients to suffer from sleep disturbances. In this paper, we propose a deep convolutional neural network (CNN) model based on the oxygen saturation (SpO₂₎ ...

The growing evolution of cyber-attacks imposes a risk in network services. The search of new techniques is essential to detect and classify dangerous attacks. In that regard, deep reinforcement learning (DRL) is emerging both as a promising ...

Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and ...

Brain tumors can be a major cause of psychiatric complications such as depression and panic attacks. Quick and timely recognition of a brain tumor is more effective in tumor healing. The processing of medical images plays a crucial role in assisting ...

In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from ...

Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. With the emergence ...

Deep learning techniques have recently demonstrated their ability to be applied in any field, including image processing, natural language processing, speech recognition, and many other real-world problem-solving applications. Human activity recognition (...

Speech emotion recognition is a significant source of information especially when other channels, like face or body, are hidden. The shape of the vocal tract, tone of the voice, pitch and other characteristics are influenced by human emotions. In ...