Early prediction of patients in need of admission to the intensive care unit (ICU) is essential for maximizing the use of available hospital resources and enhancing the quality of patient care outcomes. This work uses the Covid19MPD Dataset to predict ICU admissions based on various machine learning techniques such as Random Forest, Support Vector Machine, Gradient Boosting, and Multi-Layer Perceptron alongside Explainable Artificial Intelligence (XAI) approaches. Our findings show that the Gradient Boosting model achieved the best accuracy at 97.49% and an F1 score of 71% for ICU admissions. Notably, the study finds that age and pneumonia are important predictors, with patients 45 years and older who come with COVID-19 and pneumonia having a much higher chance of needing ICU care. These findings highlight how important it is to use machine learning models in clinical settings in order to improve ICU admission prediction and facilitate prompt medical intervention.

Throughout recent years, the field of wireless communication has experienced exponential growth. This expansion has been propelled by the continual innovation of diverse wireless standards and the evolution of high-speed applications, resulting in a mounting scarcity of spectrum and an intensified demand for bandwidth. Regrettably, existing studies substantiate an inefficient utilization of available frequency bands. Channel bandwidth and effective spectrum utilization persist as formidable challenges in the realm of wireless communication. Addressing these challenges, cognitive radio stands as a pivotal solution, enabling the efficient sharing of available spectrum among primary/secondary or licensed/unlicensed users. The successful implementation of cognitive radio relies significantly on accurate spectrum sensing and prediction to avert interference or collisions among users. This work introduces a neural network-based model augmented and fine-tuned by a genetic algorithm, exemplifying state-of-the-art effectiveness in spectrum prediction. To expand the horizon, this paper investigates a novel approach based on the radial basis function network, further enriching the exploration. The proposed model demonstrates exceptional performance as validated through rigorous MATLAB simulations. The comparative analysis of these simulations serves as a robust benchmark, illuminating the superior efficacy and practicality of the model in real-world scenarios.

ChatGPT is an extensive language model under the umbrella of generative artificial intelligence that produces answers from data and images curated from online resources. Despite the capability to produce accurate responses, but requires verification; the responses are based on statistical patterns rather than true comprehension, i.e., it does not have consciousness and does not understand the questions from the perspective of human comprehension. The ability of ChatGPT to understand and react to questions in a humanistic way has garnered a lot of public and scientific interest over the past year. This study analyzes responses of ChatGPT to 100 questions on epilepsy in order to assess the validity of the tool in this field. Besides, this work sheds light on the advantages and disadvantages of the approach in this particular topic by analyzing responses of ChatGPT to queries on epilepsy. The study evaluates the model performance by looking at the completeness, correctness, and relevancy of responses. The findings in this paper indicate that ChatGPT has limits because of its training data and design structure, even though it could give insightful and appropriate answers to inquiries about epilepsy. It is concluded that ChatGPT can be an advantageous tool for medical professionals working on the subject of epilepsy. Nonetheless, it should be noted that ChatGPT should be utilized cautiously and in conjunction with various information sources, like clinical practice guidelines and peer-reviewed studies.

Artificial Intelligence is transforming the healthcare industry due to the increasing accessibility of organized and unorganized information and the rapid development of analytical techniques. As artificial intelligence becomes more significant in healthcare, concerns are arising regarding the lack of transparency, explainability, and the possibility of bias in model predictions. The goal of this paper is to utilize interpretable machine learning to provide a better understanding of breast cancer using the Local Interpretable Model-agnostic Method (LIME). This study uses Local Interpretable Model-Agnostic Explanations to explain how the machine-learning model accurately classifies breast cancer cases as either Benign or Malignant. It provides a LIME plot for Benign cases, highlighting the significant role of "Bare Nuclei" where lower values strongly suggest Benign predictions. Other features like Normal Nucleoli, Marginal adhesion, single adhesion, Mitoses, and uniformity of cell size also contribute to the Benign class prediction when they fall below a particular threshold. The study also presents a LIME plot for Malignant cases, emphasizing the importance of "Bare Nuclei" and Clump thickness, where higher values indicate a higher likelihood of Malignant predictions. Other features like Normal Nucleoli, Marginal adhesion, Bland chromatin, uniformity of cell size, and Mitoses also contribute to Malignant predictions when their values exceed specific thresholds. The feature "Concave points_worst" influences the model’s benign predictions when it is below 0.07, while the "texture" feature affects predictions when it exceeds 29.41. Furthermore, the study provides further explanations for Malignant predictions based on "Concave points_worst" and "Texture." These findings provide valuable insights into the decision-making process of the model, making it more interpretable and useful for breast cancer diagnosis.

Deception detection plays a vital role in various domains, from security and law enforcement to human behavior analysis. In this paper, we propose a comprehensive system for deception detection that leverages S&A smart sensing device, deep transfer learning, deep learning techniques, and explainable artificial intelligence. Our approach combines visual, auditory, thermal, cardiovascular, and respiratory cues, offering enhanced accuracy and resistance to countermeasures. Deep Transfer Learning is employed to adapt pre-trained models to the deception detection task, overcoming data limitations. Incorporating Explainable AI techniques enhances transparency and interpretability, fostering trust and collaboration in human-machine interactions. Our research lays the groundwork for future advancements in deception detection technology, addressing challenges and providing promising opportunities in the realm of deception detection.