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Gender falsification in social media content is an increasingly troubling challenge, with users often choosing to hide their true gender identity or pretend to be members of a different gender. This can lead to negative consequences, including the spread of disinformation, discrimination and online security risks. To overcome this problem, this research proposes a text classification-based solution to identify gender fakes in social media texts. This method involves extracting linguistic features from texts, such as word usage, sentence structure, and language patterns that can provide clues to the author's gender. Therefore, this research aims to introduce a new transformers-based approach that uses XLNet and is also modified with additional Fasttext embedding. Modifications were made to the embedding section which can increase XLNet's understanding of text context in carrying out text classification. The results of this research are that baseline XLNet gets a fairly good performance score in gender classification based on Twitter feeds, namely with accuracy, precision, recall and f1-score of 0.704, 0.770, 0.598, 0.674 respectively, while XLNet-FastText gets the respective scores. -respectively 0.714, 0.770, 0.609, 0.680. And for gender classification based on user account descriptions, baseline XLNet gets scores of accuracy, precision, recall, f1-score of 0.705, 0.771, 0.598, 0.674 respectively while XLNet-FastText gets scores of 0.724, 0.751, 0.6324, 0.686 respectively.

The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person’s name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows.

In computer vision applications, gait-based gender classification is a challenging task as a person may walk at various angles with respect to the camera viewpoint. In some of the viewing angles, the person’s limb movement can be occluded from the camera, preventing the perception of the gait-based features. To solve this problem, this study proposes a robust and lightweight system for gait-based gender classification. It uses a gait energy image (GEI) for representing the gait of an individual. A discrete cosine transform (DCT) is applied on GEI to generate a gait-based feature vector. Further, this DCT feature vector is applied to XGBoost classifier for performing gender classification. To improve the classification results, the XGBoost parameters are tuned. Finally, the results are compared with the other state-of-the-art approaches. The performance of the proposed system is evaluated on the OU-MVLP dataset. The experiment results show a mean CCR (correct classification rate) of 95.33% for the gender classification. The results obtained from various viewpoints of OU-MVLP illustrate the robustness of the proposed system for gait-based gender classification.

The speech signal contains a vast spectrum of information about the speaker such as speakers’ gender, age, accent, or health state. In this paper, we explored different approaches to automatic speaker’s gender classification and age estimation system using speech signals. We applied various Deep Neural Network-based embedder architectures such as x-vector and d-vector to age estimation and gender classification tasks. Furthermore, we have applied a transfer learning-based training scheme with pre-training the embedder network for a speaker recognition task using the Vox-Celeb1 dataset and then fine-tuning it for the joint age estimation and gender classification task. The best performing system achieves new state-of-the-art results on the age estimation task using popular TIMIT dataset with a mean absolute error (MAE) of 5.12 years for male and 5.29 years for female speakers and a root-mean square error (RMSE) of 7.24 and 8.12 years for male and female speakers, respectively, and an overall gender recognition accuracy of 99.60%.

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.

Speech reflects people’s mental state and using a microphone sensor is a potential method for human–computer interaction. Speech recognition using this sensor is conducive to the diagnosis of mental illnesses. The gender difference of speakers affects the process of speech emotion recognition based on specific acoustic features, resulting in the decline of emotion recognition accuracy. Therefore, we believe that the accuracy of speech emotion recognition can be effectively improved by selecting different features of speech for emotion recognition based on the speech representations of different genders. In this paper, we propose a speech emotion recognition method based on gender classification. First, we use MLP to classify the original speech by gender. Second, based on the different acoustic features of male and female speech, we analyze the influence weights of multiple speech emotion features in male and female speech, and establish the optimal feature sets for male and female emotion recognition, respectively. Finally, we train and test CNN and BiLSTM, respectively, by using the male and the female speech emotion feature sets. The results show that the proposed emotion recognition models have an advantage in terms of average recognition accuracy compared with gender-mixed recognition models.

Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based approaches often suffer from high computational costs and limited interpretability, reducing their applicability in forensic investigations. This study aims to develop a multi-task deep learning framework that enhances both accuracy and explainability in CBCT-based age estimation and gender classification using attention mechanisms. We propose a multi-task learning (MTL) model that simultaneously estimates age and classifies gender using panoramic slices extracted from CBCT scans. To improve interpretability, we integrate Convolutional Block Attention Module (CBAM) and Grad-CAM visualization, highlighting relevant craniofacial regions. The dataset includes 2,426 CBCT images from individuals aged 7 to 23 years, and performance is assessed using Mean Absolute Error (MAE) for age estimation and accuracy for gender classification. The proposed model achieves a MAE of 1.08 years for age estimation and 95.3% accuracy in gender classification, significantly outperforming conventional CBCT-based methods. CBAM enhances the model’s ability to focus on clinically relevant anatomical features, while Grad-CAM provides visual explanations, improving interpretability. Additionally, using panoramic slices instead of full 3D CBCT volumes reduces computational costs without sacrificing accuracy. Our framework improves both accuracy and interpretability in forensic age estimation and gender classification from CBCT images. By incorporating explainable AI techniques, this model provides a computationally efficient and clinically interpretable tool for forensic and medical applications.

Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.

Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribute (Attribute: either Gender or Age) and Multi-Attribute (both Gender and Age) prediction model. We present a review for facial age estimation and gender classification methods based on conventional as well as deep learning approaches developed so far with analysis of their pros, cons and insights for future research. Moreover, this study also enlists the databases used for benchmarking results with their properties for both constrained and unconstrained environment.