The rapid advancement of connected vehicle technologies has intensified the need for efficient spectrum utilization. Cognitive radio (CR) enables dynamic access to underutilized spectrum, with spectrum sensing playing a key role in detecting primary users (PU). This study introduces a novel spectrum sensing approach that integrates filter bank (FB) signal decomposition with convolutional neural networks (CNNs)—referred to as filter bank decomposition and convolutional neural network (FB-CNN)—to enhance detection performance compared to conventional methods. Unlike traditional techniques such as energy detection (ED), the proposed FB-CNN leverages the frequency components extracted by the FB as CNN inputs, enabling robust identification of PU signals across multiple modulation schemes, including BPSK, QAM, FSK, and GMSK. Simulation results demonstrate substantial gains, particularly in low-SNR scenarios: for example, at an SNR of 5 dB, FB-CNN achieves a detection probability of 89% for 2-FSK, compared to only 22% with ED—representing a fourfold improvement. These findings highlight the novelty and effectiveness of FB-CNN in significantly improving spectrum sensing reliability for connected vehicle networks operating in challenging signal environments.
Existing arbitrage detection techniques rely on exhaustive search or linear programming, which are computationally expensive and often miss profitable cycles in dynamic markets. Triangular arbitrage is a profitable trading strategy that exploits discrepancies in currency exchange rates, but common algorithms detect only a limited number of loops and cannot find non-loop opportunities. To address these gaps, this study presents a realtime, graph-based framework for identifying triangular arbitrage opportunities in cryptocurrency markets using an optimized implementation of the Bellman–Ford algorithm. By modeling currency exchange rates as a directed graph and detecting negative-weight cycles, the framework efficiently identifies profitable arbitrage opportunities under realistic trading conditions. The proposed framework achieves an average detection latency of 0.002 milliseconds, providing empirical performance benchmarks for single-exchange cryptocurrency trading systems. Experiments on a six-month historical dataset yielded a detection accuracy of 92%, while additional validation on live cryptocurrency market data streams confirmed the framework’s real-time performance and low latency. This high-speed detection is crucial in high-frequency trading (HFT), where brief pricing inefficiencies can yield significant profits before being corrected. The experimental pipeline is designed to support reproducibility and comparative evaluation in applied FinTech research.
Despite growing interest in automated waste detection, existing surveys either focus on a narrow set of models or lack systematic comparisons across object detection paradigms. This review addresses that gap by examining recent advances in deep learning for waste management, spanning two-stage detectors (Faster region-based convolutional neural network (Faster R-CNN) and Mask region-based convolutional neural network (Mask R-CNN)), single-shot frameworks (you only look once version 1 (YOLO)v1 to YOLOv11), and emerging Transformer-based models (ViT-WM and AL-DETR). Faster R-CNN achieved category-level accuracy of 91.68% and overall accuracy of 89.68%, while Mask R-CNN reported AP values between 26.2% and 34.5% across varied datasets. YOLO models demonstrated strong real-time capability, with YOLOv5 reaching a mAP@0.5 of 92.96% and YOLOv8 achieving 97.63% accuracy with precision and recall above 93%. Transformer-based approaches are especially promising: ViT-WM achieved 98.17% accuracy, the highest among reviewed models, and AL-DETR reported a mAP of 58.9% while integrating active learning (AL) strategies to reduce reliance on extensive labeled data. These results emphasize YOLO’s efficiency for real-time waste sorting and the potential of Transformer architectures for handling complex, cluttered environments. Remaining challenges include dataset variability, computational demand, and limited standardized benchmarks. Future research should prioritize developing comprehensive datasets, optimizing Transformers for real-time use, and leveraging AL to enhance generalizability with reduced annotation effort.
Brain tumors are life-threatening conditions requiring accurate and timely diagnosis for effective treatment. This paper proposes a novel hybrid model combining U-Net for tumor segmentation and residual network 50 (ResNet50) architecture for classification to achieve performance in brain tumor classification from magnetic resonance imaging (MRI) images. This paper proposes a novel hybrid model that integrates U-Net for tumor segmentation with ResNet50 architecture for classification, enabling robust multi-class classification across glioma, meningioma, pituitary tumor, and no tumor classes. Utilizing a diverse dataset of 7,023 MRI images, the model achieves a remarkable accuracy of 99.78±0.05%, outperforming existing methods. Compared to related works, the proposed model demonstrates superior accuracy and scalability. This hybrid approach addresses key challenges in medical imaging, providing a robust and interpretable solution for real-world clinical applications.
This study introduces SignVerse, a novel bi-directional sign language translation (SLT) system, to enhance communication between the hearing-impaired community and the general public. SignVerse makes real-time, two-way conversations easy for Indian Sign Language (ISL) users—no special hardware needed. The system uses smart artificial intelligence (AI) tech: computer vision, deep learning, and natural language processing (NLP). When someone types or speaks, the text/speech-to-sign module runs the input through NLP-based syntactic reordering and shows the ISL translation using a lively 3D avatar. On the flip side, the sign-to-text/speech module leverages MediaPipe to spot hand landmarks in real time, and the convolutional neural network-long short-term memory (CNN-LSTM) model accurately recognizes each gesture. Everything works together to help ISL users connect smoothly with others 94.8% recognition accuracy, less than 1.8-second translation latency, and more than 90% gesture clarity in user studies are all demonstrated by experimental evaluations. The lightweight model, which is optimized through knowledge distillation, guarantees excellent performance even on common consumer devices. With significant potential for societal impact, SignVerse is a significant step toward real-time, AI-driven ISL translation. When everything is taken into account, it is a dependable, scalable, and reasonably priced choice for inclusive communication.
