Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2FP-OHDBN model initially normalizes the traffic data using min–max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634.

Ransomware remains one of the most significant cybersecurity threats. Techniques used by attackers have evolved to bypass traditional detection methods. Many existing detection systems rely on outdated datasets or complex behavioral analyses, which are resource-intensive and slow. This paper introduces RansomFormer, a Transformer-based model that is designed to detect ransomware using Portable Executable (PE) byte data combined with Application Programming Interface (API) imports or API sequence calls. The evaluation is conducted to determine whether these static and dynamic features alone can achieve high accuracy. To test this hypothesis, the largest ransomware dataset to date is collected, consisting of more than 150 ransomware families. The limitations of existing datasets, which are outdated, lack family variants, or are too small, are addressed by this dataset. RansomFormer is trained and evaluated on the following two datasets: one using static analysis (PE bytes and API imports) and another combining static and dynamic analysis (PE bytes and API sequence calls). The results demonstrate that the model achieves high accuracy, with 99.25% on the static dataset and 99.50% on the combined dataset, making RansomFormer a promising approach for ransomware detection.

Medical imaging has attracted growing interest in the field of healthcare regarding breast cancer (BC). Globally, BC is a major cause of mortality amongst women. Now, the examination of histopathology images is the medical gold standard for cancer diagnoses. However, the manual process of microscopic inspections is a laborious task, and the results might be misleading as a result of human error occurring. Thus, the computer-aided diagnoses (CAD) system can be utilized for accurately detecting cancer within essential time constraints, as earlier diagnosis is the key to curing cancer. The classification and diagnosis of BC utilizing the deep learning algorithm has gained considerable attention. This article presents a model of an improved bald eagle search optimization with a synergic deep learning mechanism for breast cancer diagnoses using histopathological images (IBESSDL-BCHI). The proposed IBESSDL-BCHI model concentrates on the identification and classification of BC using HIs. To do so, the presented IBESSDL-BCHI model follows an image preprocessing method using a median filtering (MF) technique as a preprocessing step. In addition, feature extraction using a synergic deep learning (SDL) model is carried out, and the hyperparameters related to the SDL mechanism are tuned by the use of the IBES model. Lastly, long short-term memory (LSTM) was utilized to precisely categorize the HIs into two major classes, such as benign and malignant. The performance validation of the IBESSDL-BCHI system was tested utilizing the benchmark dataset, and the results demonstrate that the IBESSDL-BCHI model has shown better general efficiency for BC classification.

Sign languages are essential for communication among over 430 million deaf and hard-of-hearing individuals worldwide. However, recognizing Arabic Sign Language (ArSL) in real-world settings remains challenging due to issues like background noise, lighting variations, and hand occlusions. These limitations hinder the effectiveness of existing systems in applications such as assistive technologies and education. To tackle these challenges, we propose ASLDetect, a new model for ArSL recognition that leverages ResNet for feature extraction and a U-Net-based architecture for accurate gesture segmentation. Our method includes preprocessing steps like resizing images to 64 × 64 pixels, normalization, and selective augmentation to improve robustness in diverse environments. We evaluated ASLDetect on two datasets: ArASL2018, which features plain backgrounds, and ArASL2021, which includes more complex and diverse environments. On ArASL2018, ASLDetect achieved an accuracy of 99.35%, surpassing ResNet34 (99.08%), T-SignSys (97.92%), and UrSL-CNN (0.98%). For ArASL2021, we applied transfer learning from our ArASL2018-trained model, significantly improving performance and reaching 86.84% accuracy-outperforming ResNet34 (82.5%), T-SignSys (58.98%), and UrSL-CNN (0.49%). These results highlight ASLDetect's accuracy, robustness, and adaptability.

Arabic sign language (ArASL) is the primary communication medium for Deaf and hard-of-hearing people across Arabic-speaking communities. Most current ArASL recognition systems are based solely on visual features and do not incorporate linguistic or semantic information that could improve generalization and semantic grounding. This paper introduces CLIP-ArASL, a lightweight CLIP-style multimodal approach for static ArASL letter recognition that aligns visual hand gestures with bilingual textual descriptions. The approach integrates an EfficientNet-B0 image encoder with a MiniLM text encoder to learn a shared embedding space using a hybrid objective that combines contrastive and cross-entropy losses. This design supports supervised classification on seen classes and zero-shot prediction on unseen classes using textual class representations. The proposed approach is evaluated on two public datasets, ArASL2018 and ArASL21L. Under supervised evaluation, recognition accuracies of $99.25\pm 0.14\%$ and $91.51\pm 1.29\%$ are achieved, respectively. Zero-shot performance is assessed by withholding $20\%$ of gesture classes during training and predicting them using only their textual descriptions. In this setting, accuracies of $55.2\pm 12.15\%$ on ArASL2018 and $37.6\pm 9.07\%$ on ArASL21L are obtained. These results show that multimodal vision–language alignment supports semantic transfer and enables recognition of unseen classes.