An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.

Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, highlighting the urgent need for robust automated breast cancer detection systems. While deep learning has demonstrated potential, many current models struggle with limited accuracy and lack of interpretability.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.

In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare.

Comparing molecular representations, e-nose signals, and other featurization, for learning to smell aroma molecules.

Recent research has attempted to predict our perception of odorants using Machine Learning models. The featurization of the olfactory stimuli usually represents the odorants using molecular structure parameters, molecular fingerprints, mass spectra, or e-nose signals. However, the impact of the choice of featurization on predictive performance remains poorly reported in direct comparative studies.

Extraction of sensing data for desired scent impressions using mass spectra of odorant molecules.

Most of the olfactory perception works focused on forward prediction of odor impression, for example, given an odorant's molecular structure parameters or the sensing data predict its odor impression. So far, mapping of mass spectrum of odorant molecules into the odor perception space (binary or continuous sensory space) has been successfully performed. However, it is difficult to predict odorant's sensing data associated with binary odor descriptors (e.

Accurate brain tumor detection using deep convolutional neural network.

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually.

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system.

Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity.

Emotion recognition is defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, "ConvNet", detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise.

Predicting individual perceptual scent impression from imbalanced dataset using mass spectrum of odorant molecules.

Predicting odor impression is considered an important step towards measuring the quality of scent in the food, perfume, and cosmetic industries. In odor impression identification and classification, the main target is to predict scent impression while identifying non-target odor impressions are less significant. However, the effectiveness of predictive models depends on the quality of data distribution.

Predicting human odor perception represented by continuous values from mass spectra of essential oils resembling chemical mixtures.

There have been recent advances in predicting odor characteristics using molecular structure parameters of chemicals. Although the molecular structure parameters are available for each chemical, they cannot be used for chemical mixtures. This study will elucidate a computational method of predicting human odor perception from the mass spectra of chemical mixtures such as essential oils.