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.

Flamingos are known for their vibrant pink and reddish hues, which are not merely aesthetic but indicative of their overall health and diet. These colors are derived from carotenoid pigments in their food sources, making coloration a vital marker for monitoring their well-being and environmental conditions. This study introduces a two-stage classification methodology designed to safeguard flamingo populations by leveraging deep learning techniques. Convolutional Neural Networks (CNNs) are used for both shape and color detection, ensuring accurate identification of flamingos and insights into their health status. Simulation results demonstrated the CNNs model’s effectiveness, making it a valuable resource for wildlife conservation efforts aimed at preserving flamingo habitats. The first stage employs digital classification filters to distinguish flamingo images from other species, achieving an accuracy of 97.52%, while the second stage refines these detections through color analysis with an accuracy of 86.27%. This approach promises to mark a significant advancement in wildlife conservation, offering reliable methods for assessing and managing flamingo populations in their natural environments.

Speech signals play a vital role in various biomedical applications, aiding in disease diagnosis, monitoring, therapy, and enhancing healthcare services. They can detect multiple medical disorders, including neurological, speech and language, respiratory, pulmonary, neuromuscular, and mental conditions. Advancements in speech analysis and processing techniques significantly contribute to patient well-being and the efficiency of healthcare delivery. This paper focuses on Parkinson’s disease as a case study to propose a comprehensive noise reduction framework combining the Hamming window technique, a Butterworth bandpass filter, and an Least Mean Squares adaptive filter. This approach achieves a substantial signal-to-noise ratio improvement of 22.6 dB, demonstrating its effectiveness in enhancing speech signal quality for biomedical applications, particularly in the context of Parkinson’s disease.

Parkinson’s disease (PD) is a progressive neurode-generative disorder that affects millions of individuals worldwide, significantly impairing their quality of life through motor symptoms such as tremors, rigidity, and particularly, Freezing of Gait (FOG). FOG is characterized by transient episodes where patients temporarily lose the ability to initiate or continue walking, posing risks of falls, loss of independence, and psychological distress. This study leverages advancements in machine learning (ML) and explainable artificial intelligence (XAI) to develop and validate a predictive model for FOG. Using a publicly available dataset and employing various ML techniques, the study evaluates the performance of these models in terms of accuracy, precision, recall, and F1-score. Incorporating XAI methods enhances the interpretability of the model, making its predictions more transparent. The results indicate that a 5-second window size with a 5-second pre-FOG period and a Random Forest classifier achieved the highest performance, with an accuracy of $\mathbf{9 9. 3 4 \%}$. Interpretability analyses, including permutation importance and Partial Dependence Plots (PDP), highlight the key features influencing predictions, such as peak frequencies and statistical measures. Future work will focus on model generalizability, exploring additional datasets to improve predictive accuracy and clinical applicability.

Wildlife conservation is a pressing global concern, with illegal poaching posing a severe threat to many endangered species. In recent years, advanced technologies like machine learning and digital signal processing have shown significant potential in supporting conservation efforts by detecting and preventing illegal poaching. This work explores the integration of these technologies into wildlife conservation strategies, focusing on their role in identifying and combating poachers. Two datasets are utilized: ‘Poacher Detection 3 Classes” and “Illegal Poacher Detection”. Several machine learning models including Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Convolutional Neural Networks (CNN) are applied to detect illegal activities in these datasets. For the first dataset, the SVM model achieved the best accuracy of ${9 3 \%}$, while the CNN model performed best on the second dataset, achieving accuracy of $75 \%$. In the final phase, both datasets were combined, and augmented images were introduced to increase data diversity. On the combined dataset, the Random Forest model achieved the highest accuracy of ${8 3 \%}$. These results demonstrate the effectiveness of machine learning in improving wildlife conservation efforts.

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.