Search Results for author:
Found 16 papers, 3 papers with code
A Deep Learning Method for Classification of Biophilic Artworks
Biophilia is an innate love for living things and nature itself that has been associated with a positive impact on mental health and well-being.
FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision
We show that from the point of the target class selection, we make an assumption on the prediction process, hence neglecting a large portion of the predictor CNN model's thinking process.
Real-time Detection of AI-Generated Speech for DeepFake Voice Conversion
There are growing implications surrounding generative AI in the speech domain that enable voice cloning and real-time voice conversion from one individual to another.
CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
Initially, a synthetic dataset is generated that mirrors the ten classes of the already available CIFAR-10 dataset with latent diffusion which provides a contrasting set of images for comparison to real photographs.
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification
Two robots are then tasked with forging 50 signatures, where 25 are used for the verification attack, and the remaining 25 are used for tuning of the model to defend against them.
Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor
Features from each set are compared by their ANOVA F-Scores and p-values, arranged into bins grown by 10 features per step to a limit of the 250 highest-ranked features.
Reducing Overconfidence Predictions for Autonomous Driving Perception
In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs.
Improving Customer Service Chatbots with Attention-based Transfer Learning
With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial to the social domain.
Continuation of Famous Art with AI: A Conditional Adversarial Network Inpainting Approach
Much of the state-of-the-art in image synthesis inspired by real artwork are either entirely generative by filtered random noise or inspired by the transfer of style.
Fruit Quality and Defect Image Classification with Conditional GAN Data Augmentation
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or gangrenous.
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification
We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4. 01%.
Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines
The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89. 27% and 93. 72%, respectively.
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity
In speech recognition problems, data scarcity often poses an issue due to the willingness of humans to provide large amounts of data for learning and classification.
On the effects of pseudorandom and quantum-random number generators in soft computing
In CIFAR-10, the QRNG outperforms PRNG by + 0. 92%.
On the Effects of Pseudo and Quantum Random Number Generators in Soft Computing
In 50 Dense Neural Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at +0. 1%, and QRNG exceeded PRNG for mental state EEG classification by +2. 82%.
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks.
