Mohammad Asadpour

This paper proposes a multiscale residual framework for gender classification on fingerprint images. It addresses the tough problem where most gender-related features in fingerprint ridges are subtle, and previous methods relied mainly on spatial features and failed to utilize the multi-scale features, attention, and dimension reduction. Therefore, morphological preprocessing, discrete wavelet-based multiscale feature extraction, Siamese network-based dimensionality reduction, and residual neural network classification, involving attention and non-local block, is proposed to effectively solve this problem. This framework performs morphological preprocessing on fingerprint images and decomposes the image into low-, mid-, and high-frequency channels by discrete wavelet transform; then, these channels are embedded into feature vectors by a Siamese ResNeXt-SE structure, and their cross-attention is fused to retain inter-/intra-class discriminability. Then, it applies a residual neural network with attention and a non-local block on the features for classification. On the SOCOFing dataset, which has 6,000 fingerprint images, the accuracy, precision, recall, and F1-score are 99.50%, 99.68%, 98.82%, and 0.99, respectively, and AUC is 0.99 for both genders, which achieves excellent results. Ablation studies have proven that morphological operators, wavelet decomposition, and Siamese compression do contribute to the framework individually. Experiments against two common types of noises, Gaussian and salt-and-pepper, also demonstrate the robustness against noise with trivial loss of accuracy, and comparing with prior CNN and CNN-SVM models, the method shows superiority by utilizing multiscale and frequency domain information comprehensively. However, it is constrained to use high-quality grayscale images and is not tested on adversarial robustness. The framework shows high potential for applications such as automatic fingerprint identification systems, forensics, and large biometric databases since the gender classification is effective, scalable, and discriminative.

Target localization (positioning) by the time difference of arrival (TDoA) using previously localized wireless sensor nodes has wide applications. Three‐degree sensing coverage cannot calculate accurate spatial‐temporal information of the target object in two‐dimensional acoustic positioning. In this article, algebraic and geometrical modeling of the two‐dimensional acoustic positioning of the target has been reviewed. By using these models, it is proved that the sensing area with three‐degree sensing coverage can be separated into two different zones. In the Type 1 zone, which is continuous, the target can be precisely positioned. Type 2 zones, which are three separate areas with three‐degree sensing coverage, cannot accurately position the target, and there is a need for four‐degree sensing coverage. Then, a relationship is presented to define the exact boundaries of these two zones. In the following, more features of this relationship have been examined. The results of this article can be used to improve the quality of the service metrics of the wireless sensor network, such as the accuracy of acoustic target positioning and tracking, along with reducing the cost of system implementation and increasing the lifetime of the network by optimal placement of wireless sensor nodes.

Some texts that are challenging to recognize on their own may become more understandable in a neighborhood of related texts with similar contexts. Motivated by this intuition, a novel deep text sentiment classification (DTSC) model is proposed to improve the model’s performance by incorporating the neighborhood of related texts. Our framework uses a nonparametric approach to construct neighborhoods of related texts based on Jaccard similarities. Then, a new deep recurrent neural network architecture is proposed, comprising two distinct modules: bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU). The proposed model aims to effectively capture informative features from the input text and its neighbors. The result of each module is processed through the maximum operation, which selects the most pertinent data. Finally, the extracted features are concatenated and subjected to classification to achieve accurate sentiment prediction. Previous studies have commonly employed a parametric approach to represent textual metadata. However, our approach utilizes a nonparametric approach, enabling our model to perform strongly even when the text vocabulary varies between training and testing. The proposed DTSC model has been evaluated on five real-world sentiment datasets, achieving 99.60% accuracy on the Binary_Getty (BG) dataset, 98.32% accuracy on the Binary_iStock (BIS) dataset, 96.13% accuracy on Twitter, 82.19% accuracy on the multi-view sentiment analysis (MVSA) dataset, and 87.60% accuracy on the IMDB dataset. These findings demonstrate that the proposed model outperforms established baseline techniques in terms of model evaluation criteria for text sentiment classification.

Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions. Consequently, automated sentiment analysis (SA) is critical for recognising people's feelings in ways that other information sources cannot. The analysis of these feelings revealed various applications, including brand evaluations, YouTube film reviews and healthcare applications. As social media continues to develop, people post a massive amount of information in different forms, including text, photos, audio and video. Thus, traditional SA algorithms have become limited, as they do not consider the expressiveness of other modalities. By including such characteristics from various material sources, these multimodal data streams provide new opportunities for optimising the expected results beyond text-based SA. Our study focuses on the forefront field of multimodal SA, which examines visual and textual data posted on social media networks. Many people are more likely to utilise this information to express themselves on these platforms. To serve as a resource for academics in this rapidly growing field, we introduce a comprehensive overview of textual and visual SA, including data pre-processing, feature extraction techniques, sentiment benchmark datasets, and the efficacy of multiple classification methodologies suited to each field. We also provide a brief introduction of the most frequently utilised data fusion strategies and a summary of existing research on visual-textual SA. Finally, we highlight the most significant challenges and investigate several important sentiment applications.