Automated crack localization for road safety using contextual u-net with spatial-channel feature integration.

Accurate and timely crack localization is crucial for road safety and maintenance, but image processing and hand-crafted feature engineering methods, often fail to distinguish cracks from background noise under diverse lighting and surface conditions. This paper proposes a framework utilizing contextual U-Net deep learning model to automatically localize cracks in road images. The framework design considers crack localization as a task of pixel-level segmenting, and analyzing each pixel in a road image to determine if it belongs to a crack or not.

Urban small-scale hydroponics: A compact, smart home-based hydroponics system.

The increasing population and urbanization have created a massive gap in the demand-supply model of food grains. The world is facing an acute problem with global warming and EI Nino effects, which have affected the equilibrium of the food chain. It is a need of the hour to introduce new reforms in farming to reap increased yields and reduce dependency on natural resources.

Behavioral profiling for adaptive video summarization: From generalization to personalization.

In today's world of managing multimedia content, dealing with the amount of CCTV footage poses challenges related to storage, accessibility and efficient navigation. To tackle these issues, we suggest an encompassing technique, for summarizing videos that merges machine-learning techniques with user engagement. Our methodology consists of two phases, each bringing improvements to video summarization.

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks.

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics.

Multimodal Abstractive Summarization using bidirectional encoder representations from transformers with attention mechanism.

In recent decades, abstractive text summarization using multimodal input has attracted many researchers due to the capability of gathering information from various sources to create a concise summary. However, the existing methodologies based on multimodal summarization provide only a summary for the short videos and poor results for the lengthy videos. To address the aforementioned issues, this research presented the Multimodal Abstractive Summarization using Bidirectional Encoder Representations from Transformers (MAS-BERT) with an attention mechanism.