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Found 16 papers, 2 papers with code
Employing Multimodal Machine Learning for Stress Detection
In this work, a multimodal AI-based framework is proposed to monitor a person's working behavior and stress levels.
WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection.
Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy
In this work, we present an approach based on transfer learning, from a model trained on a secondary dataset, for the real time deployment of the depression screening tool based on the actigraphy data of users.
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems
Modern applications such as self-driving cars and drones rely heavily upon robust object detection techniques.
In Rain or Shine: Understanding and Overcoming Dataset Bias for Improving Robustness Against Weather Corruptions for Autonomous Vehicles
Methods to mitigate such bias which allows the OD models to perform better and improve the robustness are also demonstrated.
Explainable Misinformation Detection Across Multiple Social Media Platforms
In this work, the integration of two machine learning approaches, namely domain adaptation and explainable AI, is proposed to address these two issues of generalized detection and explainability.
A comprehensive survey on computational learning methods for analysis of gene expression data
We discuss the types of missing values, and the methods and approaches usually employed in their imputation.
Incremental learning of LSTM framework for sensor fusion in attitude estimation
This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network.
Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directions
We present the comprehensive taxonomy of multimodal co-learning based on the challenges addressed by co-learning and associated implementations.
Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation
The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates.
Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential.
Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media
Credibility is the believability of the piece of information at hand.
A Review on Explainability in Multimodal Deep Neural Nets
Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks.
Comparison of machine learning and deep learning techniques in promoter prediction across diverse species
In this work, we studied methods for vector encoding and promoter classification using genome sequences of three distinct higher eukaryotes viz.
Role of Artificial Intelligence in Detection of Hateful Speech for Hinglish Data on Social Media
Hate speech detection algorithms deployed by most social networking platforms are unable to filter out offensive and abusive content posted in these code-mixed languages.
ARiA: Utilizing Richard's Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets
This work introduces a novel activation unit that can be efficiently employed in deep neural nets (DNNs) and performs significantly better than the traditional Rectified Linear Units (ReLU).
