The predominant means of communication is speech; however, there are persons whose speaking or hearing abilities are impaired. Communication presents a significant barrier for persons with such disabilities. The use of deep learning methods can help to reduce communication barriers. This paper proposes a deep learning-based model that detects and recognizes the words from a person’s gestures. Deep learning models, namely, LSTM and GRU (feedback-based learning models), are used to recognize signs from isolated Indian Sign Language (ISL) video frames. The four different sequential combinations of LSTM and GRU (as there are two layers of LSTM and two layers of GRU) were used with our own dataset, IISL2020. The proposed model, consisting of a single layer of LSTM followed by GRU, achieves around 97% accuracy over 11 different signs. This method may help persons who are unaware of sign language to communicate with persons whose speech or hearing is impaired.

Sign language is the most common form of communication for the deaf and dumb. To bridge the communication gap with such impaired people, normal people should be able to recognize signs. Therefore, it is necessary to introduce a sign language recognition system to assist such impaired people. This paper proposes the Transformer Encoder as a useful tool for sign language recognition. For the recognition of static Indian signs, the authors have implemented a vision transformer. To recognize static Indian sign language, proposed methodology archives noticeable performance over other state-of-the-art convolution architecture. The suggested methodology divides the sign into a series of positional embedding patches, which are then sent to a transformer block with four self-attention layers and a multilayer perceptron network. Experimental results show satisfactory identification of gestures under various augmentation methods. Moreover, the proposed approach only requires a very small number of training epochs to achieve 99.29 percent accuracy.

Deep learning has significantly aided current advancements in artificial intelligence. Deep learning techniques have significantly outperformed more than typical machine learning approaches, in various fields like Computer Vision, Natural Language Processing (NLP), Robotics Science, and Human-Computer Interaction (HCI). Deep learning models are somewhat ineffective in outlining their fundamental mechanism. That’s the reason the deep learning model mainly consider as Black-Box. To establish confidence and responsibility, deep learning applications need to explain the model’s decision in addition to the prediction of results. The explainable AI (XAI) research has created methods that offer these interpretations for already trained neural networks. It’s highly recommended for computer vision tasks relevant to medical science, defense system, and many more. The proposed study is associated with XAI for Sign Language Recognition, the methodology uses an attention-based ensemble learning approach to create a prediction model more accurate. Methodology uses ReSNet50 and Self Attention model to design ensemble learning architecture. The proposed ensemble learning approach has achieved remarkable accuracy at 98.20%. Interpret ensemble learning prediction, the author has proposed SignExplainer to explain the relevancy (in percentage) of predicted results. SignExplainer has illustrated excellent results, compare to other conventional Explainable AI models.

Sign language is a common way of communication for people with hearing and/or speaking impairments. AI-based automatic systems for sign language recognition are very desirable since they can reduce barriers between people and improve Human-Computer Interaction (HCI) for the impaired community. Automatically recognizing sign language is still an open challenge since the sign language itself has a complex structure to convey messages. The key role is played by the isolated signs that refer to single gestures carried out by hand movements. In the last decade, research has improved the automatic recognition of isolated sign language from videos using machine learning approaches. Starting from a comprehensive analysis of existing recognition techniques, with an in-depth focus on existing public datasets, the study proposes an advanced convolution-based hybrid Inception architecture to improve the recognition accuracy of isolated signs. The main contributions are to enhance InceptionV4 with optimized backpropagation through uniform connections. Besides, an ensemble learning framework with different Convolution Neural Networks has been also introduced and exploited to further increase the recognition accuracy and robustness of isolated sign language recognition systems. The effectiveness of the proposed learning approaches has been proved on a benchmark dataset of isolated sign language gestures. The experimental results demonstrate that the proposed ensemble model outperforms sign identification, yielding higher recognition accuracy (98.46%) and improved robustness.

Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method’s performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies.

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary cause of DR in patients is high blood sugar, and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and their automation applications can recognize the presence of DR in retinal cells and vessel images. Authors have proposed an attention-based hybrid model to recognize diabetes in early stage to prevent harmful clauses. Proposed methodology uses DenseNet121 architecture for convolution learning and then, the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates binary and multiclass classification to recognize the infection and the spreading of disease. Binary classification recognizes DR images either positive or negative, while multiclass classification represents an infection on a scale of 0–4. Simulation of the proposed methodology has achieved 98.57% and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention-based deep learning model has achieved remarkable accuracy to detect diabetic infection from retinal cellular images.