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Tanzeela Bibi
Nanjing University of Information Science and Technology
Engineering

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AVNPR-Net: A Real-Time Deep Learning Framework for Robust Vehicle Number Plate Detection and Recognition Ahmad Ijaz; Tayyba Sarfraz; Tanzeela Bibi; Muhammad Usman
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.495

Abstract

AVNPR systems are critical in intelligent transportation, monitoring, and law enforcement systems. Nevertheless, the current systems are usually challenged by the issues of dissimilar illumination, obstruction, and the diversity of plate formats, which restrict their practical applicability. To solve these problems, this paper suggests a real-time deep learning-driven AVNPR framework that incorporates effective detection and recognition systems.  The proposed system employs the YOLOv8 object detector model to localize number plates with high accuracy and speed, as well as a lightweight recognition module to identify alphanumeric characters. A custom dataset with different types of vehicles in different environmental conditions was created and improved with the help of preprocessing and data augmentation methods to make the model more robust. In the experiments, the proposed system demonstrated an overall system accuracy of 98.7%, representing the combined number plate detection and character recognition results. The mAP@0.5 is 97%, and mAP 0.5-0.95 is 91%, as well as high precision, recall, and F1-score, which suggests that it shows potential applicability across varying conditions in the assessed dataset and suggests that it may be suitable for real-world applications. The system is also implemented with a Flask-based web application, and it supports image based and real-time webcam detection. The results indicate that the proposed framework provides a viable, efficient, and deployable solution to AVNPR applications. The work will lead to the creation of scalable and real-time intelligent transportation systems and give a basis for future advancement in the improvement of robust vehicle recognition in challenging conditions.
Neural Differential Cryptanalysis of GIFT-128 and ASCON via Deep Learning Muhammad Ahmad; Hua Zhou; Muhammad Usman; Tanzeela Bibi; Haider Ali; Maryum Shahzadi; Farah Javed
Scientific Journal of Engineering Research Vol. 2 No. 4 (2026): December (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i4.2026.491

Abstract

Differential analysis is a pivotal method for assessing the security of block ciphers; it distinguishes a cipher from a random permutation by tracing the propagation of plaintext differences. Traditional analytical methods face limitations when applied to complex algorithms, whereas the feature extraction capabilities of deep learning have opened up new avenues for cryptanalysis. To facilitate the security assessment of block ciphers, this paper proposes a novel construction method for a neural differential distinguisher that integrates traditional differential analysis with deep learning techniques. Regarding dataset construction, a multi-ciphertext-pair triplet input format is adopted to preserve differential features while capturing correlations across ciphertext pairs. The network architecture is based on Convolutional Neural Networks (CNNs) and incorporates a Residual Shrinkage Network to construct a deep dilated structure and a multi-scale feature fusion mechanism. Experimental results on the GIFT-128 and ASCON-PERMUTATION lightweight permutation-based cryptographic algorithm demonstrate the efficacy of this approach: for GIFT-128, the 6-round distinguisher reached a maximum accuracy of 99.70%, and the 7-round distinguisher reached 95.47% when using 32 ciphertext pairs; for the 4-round analysis of ASCON, the accuracy rate reached a maximum of 53.54%. These results validate the effectiveness of deep learning methods in the analysis of cryptographic security.
Hybrid Machine Learning Framework for Joint Prediction of Window Mean and Bit Error Rate in SC-LDPC Decoding Tanzeela Bibi; Hua Zhou; Sana Akbar; Lalit Awasthi
Scientific Journal of Engineering Research Vol. 2 No. 1 (2026): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i1.2026.364

Abstract

Modern low-latency communication systems increasingly rely on spatially coupled low-density parity-check (SC-LDPC) codes combined with windowed decoding (WD) to achieve high reliability with reduced latency and memory requirements. However, evaluating the intrinsic trade-off between decoding complexity and error performance typically measured by the average window iteration count (WMEAN) and bit error rate (BER) still depends on computationally intensive Monte Carlo simulations, which limits rapid system optimization and real-time design exploration. To address this limitation, this paper proposes a hybrid machine learning framework for the joint, non-iterative prediction of WMEAN and BER using a single set of code and channel parameters. A high-fidelity dataset is generated through extensive SC-LDPC windowed decoding simulations across varying window sizes, coupling lengths, and signal-to-noise ratio (SNR) conditions. Based on this dataset, a multi-output Random Forest Regressor is trained to exploit the shared underlying decoding dynamics that govern both computational complexity and decoding reliability. The proposed model achieves accurate simultaneous prediction of WMEAN and BER, demonstrating strong generalization performance while significantly reducing system evaluation time compared to conventional simulation-based approaches. Feature-importance analysis further reveals the dominant influence of channel quality and coupling structure on both decoding effort and error performance. These results indicate that the proposed framework provides an effective surrogate modeling tool for fast design-space exploration and informed performance–complexity trade-off analysis. The methodology enables practical optimization of high-throughput SC-LDPC decoders and supports the development of adaptive and resource-efficient communication systems.
Co-Authors Ahmad Ijaz Farah Javed Haider Ali Hua Zhou Hua Zhou Lalit Awasthi Maryum Shahzadi Muhammad Ahmad Muhammad Usman Sana Akbar Tayyba Sarfraz