Real-Time Panorama and Image Stitching with Surf-Sift Features

Weather is influenced by several factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth's rotation. Forecasting weather with high geographic precision is both challenging and computationally demanding. This study explores a nowcasting method for meteorological radar images. Building on the concept of an unsupervised representation problem in deep learning, next-frame prediction is an emerging area in computer vision where future images are predicted based on previous ones. This has numerous applications, including in robotics and autonomous driving. The paper reviews the latest next-frame prediction networks, categorizing them into two main approaches: machine learning and deep learning. It compares these strategies by discussing their respective advantages and disadvantages. Lastly, the paper outlines the most promising directions for future research.

A Review on Rainfall Nowcasting Methodology using different ML and DL Models

Amit Solanki,

Dr. Sheshang Degadwala

2024

A Review on Fake News Detection using Deep Learning Methods

Jayshree Kathiriya,

Dr. Sheshang Degadwala

2024

The proliferation of fake news in online media platforms poses a significant threat to the integrity of information dissemination and public discourse. In response, researchers have increasingly turned to deep learning techniques to develop effective solutions for detecting and mitigating the spread of fake news. This review paper provides a comprehensive overview of recent advances in fake news detection using deep learning methodologies. We survey the literature on various deep learning architectures and approaches employed for fake news detection, including supervised, semi-supervised, and unsupervised learning methods. We discuss the challenges associated with data preprocessing, feature extraction, and model evaluation, and examine the ethical considerations and societal implications of deploying deep learning models for fake news detection. Furthermore, we identify emerging trends and future research directions in the field, with a focus on addressing the evolving nature of fake news and enhancing the robustness and interpretability of detection systems. This review contributes to the ongoing discourse on fake news detection and provides valuable insights for researchers, practitioners, and policymakers working in the domain of information integrity and online media governance.

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Degadwala,

Degadwala

2024

Medical image classification, a critical component in medical diagnostics, has significantly advanced through the integration of machine learning (ML) and deep learning (DL) techniques. This review comprehensively explores the evolution, methodologies, and applications of ML and DL in medical image classification. Traditional ML methods, including support vector machines and decision trees, have provided a foundation for early advancements by utilizing handcrafted features. However, the advent of DL, particularly convolutional neural networks (CNNs), has revolutionized the field by enabling automatic feature extraction and achieving superior performance. This review examines various DL architectures, such as ResNet, VGG, and Inception, highlighting their contributions to tasks like tumor detection, organ segmentation, and disease classification. Furthermore, it addresses challenges like data scarcity, interpretability, and computational demands, discussing potential solutions like data augmentation, transfer learning, and model optimization. The review also considers the ethical implications and the need for robust validation to ensure clinical applicability. Through a comparative analysis of existing studies, this review underscores the transformative impact of ML and DL on medical imaging, emphasizing the continuous need for innovation and interdisciplinary collaboration to enhance diagnostic accuracy and patient outcomes.