Machine Learning Models for Predicting Corticosteroid Therapy Necessity in COVID-19 Patients: A Comparative Study

This study analyzes machine learning algorithms to predict the need for corticosteroid (CS) therapy in COVID-19 patients based on initial assessments. Using data from 1861 COVID-19 patients, parameters like blood tests and pulmonary function tests were examined. Decision Tree and XGBoost emerged as top performers, achieving accuracy rates of 80.68% and 83.44% respectively. Multilayer Perceptron and AdaBoost also showed competitive performance. These findings highlight the potential of AI in guiding CS therapy decisions, with Decision Tree and XGBoost standing out as effective tools for patient identification. This research offers valuable insights for personalized medicine in infectious disease management

Elon Musk’s Neuralink Brain Chip: A Review on ‘Brain-Reading’ Device

With its novel bidirectional communication method, Neuralink, the brain-reading gadget created by Elon Musk, is poised to transform human-machine relations. It represents a revolutionary combination of health science, neurology, and artificial intelligence. Neuralink is a potentially beneficial brain implant that consists of tiny electrodes placed behind the ear and a small chip. It can be used to treat neurological conditions and improve cognitive function. Important discussions are nevertheless sparked by ethical worries about abuse, privacy, and security. It is important to maintain a careful balance between the development of technology and moral issues, as seen by the imagined future in which people interact with computers through thinking processes. In order for Neuralink to be widely accepted and responsibly incorporated into the fabric of human cognition and connectivity, ongoing discussions about ethical standards, regulatory frameworks, and societal ramifications are important. Meanwhile, new advancements in Brain-Chip-Interfaces (BCHIs) bring the larger context into focus. By enhancing signal transmission between nerve cells and chips, these developments offer increased signal fidelity and improved spatiotemporal resolution. The potential revolutionary influence of these innovations on neuroscience and human-machine symbiosis raises important considerations about the ethical and societal consequences of these innovations

Advancements and Applications of Generative Artificial Intelligence and Large Language Models on Business Management: A Comprehensive Review

This comprehensive review delves into the landscape and recent advancements of Generative Artificial Intelligence (AI) and Large Language Models (LLMs), shedding light on their transformative potential and applications across various sectors. Generative AI, exemplified by models like ChatGPT, DALL-E, and Midjourney, has rapidly evolved and is driven by breakthroughs in deep learning architectures and the availability of vast datasets. Concurrently, LLMs have revolutionized natural language processing tasks, utilizing vast text corpora to generate human-like text. The study explores recent developments, including the introduction of advanced models like GPT-4 and PaLM2 and the emergence of specialized LLMs like small LLMs (sLLMs), aimed at overcoming hardware limitations and cost constraints. Additionally, the expanding applications of generative AI, from healthcare to finance, underscore its transformative potential in addressing real-world challenges. Through a comprehensive analysis, this research contributes to the ongoing discourse on AI ethics, governance, and regulation, emphasizing the importance of responsible innovation for the benefit of humanity

Transforming Breast Cancer Identification: An In-Depth Examination of Advanced Machine Learning Models Applied to Histopathological Images

Breast cancer stands as one of the most prevalent and perilous forms of cancer affecting both women and men. The detection and treatment of breast cancer benefit significantly from histopathological images, which carry crucial phenotypic information. To enhance accuracy in breast cancer detection, Deep Neural Networks (DNNs) are commonly utilized. Our research delves into the analysis of pre-trained deep transfer learning models, including ResNet50, ResNet101, VGG16, and VGG19, for identifying breast cancer using a dataset comprising 2453 histopathology images. The dataset categorizes images into two groups: those featuring invasive ductal carcinoma (IDC) and those without IDC. Through our analysis of transfer learning models, we observed that ResNet50 outperformed the other models, achieving impressive metrics such as accuracy rates of 92.2%, Area under Curve (AUC) rates of 91.0%, recall rates of 95.7%, and a minimal loss of 3.5%

Deep Learning Models for Stock Market Forecasting: A Comprehensive Comparative Analysis

This study presents a comprehensive comparative analysis of deep learning models for stock market forecasting using data from two prominent stock exchanges, the National Stock Exchange (NSE) and the New York Stock Exchange (NYSE). Four deep neural network architectures—Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—were trained and tested on NSE data, focusing on Tata Motors in the automobile sector. The analysis included data from sectors such as Automobile, Banking, and IT for NSE and Financial and Petroleum sectors for NYSE. Results revealed that the deep neural network architectures consistently outperformed the traditional linear model, ARIMA, across both exchanges. The Mean Absolute Percentage Error (MAPE) values obtained for forecasting NSE values using ARIMA were notably higher compared to those derived from the neural networks, indicating the superior predictive capabilities of deep learning models. Notably, the CNN architecture demonstrated exceptional performance in capturing nonlinear trends, particularly in recognizing seasonal patterns within the data. Visualizations of predicted stock prices further supported the findings, showcasing the ability of deep learning models to adapt to dynamic market conditions and discern intricate patterns within financial time series data. Challenges encountered by different neural network architectures, such as difficulties in recognizing certain patterns within specific timeframes, were also analyzed, providing insights into the strengths and limitations of each model