Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones—like cloud formations, circular symmetry, and dynamic atmospheric fluctuations—the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones.

This research paper delves into the realm of automated interview evaluation, employing state-of-the-art natural language processing models. The primary focus lies in comparing the effectiveness of TF-IDF, BERT, RoBERTa, Jaccard similarity, and Word2Vec models in assessing interview responses. The paper commences by providing a comprehensive definition of automated interview evaluation, highlighting the significance of efficient and unbiased candidate assessment. The proposed methodology involves the utilization of RoBERTa, a robust transformer-based model, to analyze and score interview responses. Through meticulous experimentation and evaluation, the research scrutinizes the performance of each model, examining its ability to capture contextual nuances and semantic understanding. The final results reveal the superior efficacy of RoBERTa over the other models, demonstrating its proficiency in evaluating interview responses and emphasizing its potential for enhancing automated hiring processes. This study contributes valuable insights into the evolving field of natural language processing and automated recruitment systems.

The severe potential of cyclones presents a serious risk to infrastructure, property, and human life. For disaster planning and prevention strategies, early and accurate cyclone identification is essential. This work presents a new method for super cyclone detection based on the object detection model YOLOv8 (You Only Look Once version 8). Numerical weather prediction (NWP) models are used in the traditional method of cyclone detection. Although NWP models are useful, they have drawbacks, especially when it comes to long-term trajectory prediction. YOLOv8, a deep learning-based object detection model, is utilized to deliver precise and real-time cyclone detection. The proposed method involves the collection of meteorological satellite images and their preprocessing to create a labeled dataset for training. The YOLOv8 model is then fine-tuned on this dataset, enabling it to detect cyclones in these images. The model is very promising for cyclone identification since it can produce predictions without making strict assumptions. We use several metrics, including the confusion matrix, F1-confidence curve, recall confidence curve, precision confidence curve, precision-recall curve, and training and validation loss, to thoroughly evaluate the model’s performance. The results of the research work demonstrate the effectiveness of YOLOv8 in super cyclone detection, achieving high accuracy and real-time capabilities. By adopting this deep learning approach, we aim to enhance early warning systems and improve disaster management for super cyclones, ultimately contributing to increased safety and reduced damages caused by these catastrophic weather events. The research work showcases the potential of YOLOv8 as a valuable tool in cyclone detection and tracking, advancing the field of meteorological forecasting and disaster preparedness.

In this digital world, everything is getting online this made user-information a vital asset. A service provider needs to communicate a user that, how this data

Heart diseases are a global leading cause of death, affecting nations universally. Early detection and machine learning assistance can mitigate mortality despite medical complexities. Hence, timely diagnosis is crucial to prevent heart attacks. This study aims to identify a machine-learning method that strikes a balance between computational efficiency and prediction accuracy when it comes to heart disease prognosis. This work proposes a model integrating various methods for effective heart disease prediction. Employing efficient data collection, pre-processing, and transformation, our model uses an ensemble approach that includes Six classification machine learning algorithms- SVM, DT, RF, LR, KNN, ANN, and PCA. The study identifies significant heart disease features, predicting the highest risk through machine learning. Experimental outcomes indicate prediction accuracy obtained by SVM, DT, RF, LR, KNN, ANN, and PCA. as a feature selection process are 91%, 90%, 86%, 91%, 88%, and 91%. Collaboration is vital for enhancing algorithm precision in clinical settings. The application of machine learning to cardiovascular health has the potential to transform continuous management, personalized treatment, and early diagnosis. Robust clinical tools for better patient outcomes and public health are ensured by addressing issues like data privacy and model interpretability and by fostering interdisciplinary collaboration.