A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction.

Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessary solutions more effectively and instantly with the help of artificial intelligence (AI) and machine learning (ML).

Enhanced spectrum sensing for 5G and LTE signals using advanced deep learning models and hyperparameter tuning.

This paper introduces a novel approach to enhancing spectrum sensing accuracy for 5G and LTE signals using advanced deep learning models, with a particular focus on the impact of systematic hyperparameter tuning. By leveraging state-of-the-art neural network architecture, namely DenseNet121 and InceptionV3-the study aims to overcome the limitations of traditional spectrum sensing methods in highly dynamic and noisy wireless environments. The research highlights that, through rigorous hyperparameter optimization, these models achieved substantial improvements in detection accuracy, reaching 97.

Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation.

Electric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance.

Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights.

Breast cancer (BCa) poses a severe threat to women's health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis.

Exploring the effects of pandemics on transportation through correlations and deep learning techniques.

The COVID-19 pandemic has had a significant impact on human migration worldwide, affecting transportation patterns in cities. Many cities have issued "stay-at-home" orders during the outbreak, causing commuters to change their usual modes of transportation. For example, some transit/bus passengers have switched to driving or car-sharing.

Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization.

Diagnosing a brain tumor takes a long time and relies heavily on the radiologist's abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifying brain tumors that were both accurate and fast.