Object Detection: Surveillancing Cities for Citizens’ Safety and Protection Using Advanced Deep Learning Tools

Weapon incidents result in casualties and loss of precious human lives every year. According to the Center for Disease Control (CDC) report, over 18500 people died in year 2025 in the USA due to gun violence. It is a well-known fact that the death rate from weaponization is increasing significantly. Firearm‑related violence affects every aspect of human security, from personal safety issues like domestic violence and road rage to broader social and financial consequences. It can also escalate into large‑scale armed conflicts that cause massive violence and account for many deaths. It is evident to surveillance our cities and societies for weapon objects using artificial intelligence (AI) and deep machine learning tools. The existing machine learning tools are not efficient at detecting different kinds of weapons. In this research, the contribution is threefold. Our first contribution is that we thoroughly investigated weapon detectio systems that uses deep learning and can based automated weapon detection system that allows the system to detect multiple weapons at the same time such as knife, handgun, and rifle automatically. We have used You Only Look Once (YOLO) v4 and Convolutional Neural network (CNN) for our experimentation. We have obtained several weapon images which are publicly available and have developed the datasets. Secondly, we have compared the performance of our deep learning models on multiple combinations of datasets (fewer images to several thousand images. The experimental evaluation has shown that the YOLOv4 outperformed the CNN. Lastly, we have proposed a method to improve the accuracy of the CNN and this has been accomplished by adding N number of CNN layers N times where N = {1,3,5,...19}. This has resulted in reducing the complexity of the model and improving the accuracy and efficiency. The proposed model can be applied on surveillance systems, closed-circuit televisions and other object detection systems and many human lives can be saved by timely detection of weapons

A Digital Twin-Inspired Hybrid Variational Quantum Reinforcement Learning Framework for Heart Disease Risk Prediction

Despite many advancements in the medicine domain heart disease mortality rates continue climb that is signailing an urgent need for more advanced risk models. In this work we have tackled this challenge by introducing a hybrid framework that combines digital twin modeling with variational quantum reinforcement learning(VQRL). By developing patient-specific digital twins from raw patient clinical data, we are able to simulate unique health states within a specilized reinforcement learning environment. To optimize the diganostic decision-making we leveraged a variational quantum network. This setup utilizes a hybrid quantum-classical optimization loop, making it well suited for the constraints of modern near-term quantum hardware. When put to the test on the "Cleveland Heart Disease dataset" the proposed framework delivered impressive results: achieving a 94.2% accuracy, and an MCC of 0.82. Beyond just high scores in precision 90.4%, and recall 92.2%. Our framework demonstrated remarkable stability and an ability to generalize during the training phase. Ultimately, this fusion of digital twins and quantum computing offers a fresh, scalable path forward for clinical decision making

PSL SignBank: A Multimodal Machine Readable Dictionary for Pakistan Sign Language

The deaf community in Pakistan faces significant communication barriers due to the absence of standardized, machine-readable resources for Pakistani Sign Language (PSL). To address this challenge, SignBank for Pakistani Sign Language (PSL SignBank) has been developed as a machine-readable dictionary to preserve and promote PSL. The corpus includes 300 commonly used English words. Each word is translated into Urdu and encoded using HamNoSys notation language-independent phonetic transcription system. Each entry of dictionary integrate multiple modalities including English word, Urdu translation, HamNoSys vector representation, human signer video, and avatar-generated animation via SiGML (Signing Gesture Markup Language) rendering. The development of corpus involved systematic video recording with deaf participants from multiple institutions. This procedure was followed by the team of three sign language experts and two interpreters who verified gestural accuracy in the shape, movement, and location parameters of the hand. Compared to traditional video-based dictionaries, PSL SignBank achieved approximately 95% storage reduction with HamNoSys notation requiring around 1 KB per sign versus 1 MB for video and supports scalable sentence-level translation through the concatenation of machine-readable notations. The avatar-based rendering system was validated against human signer videos which confirmed accurate gesture reproduction for both static and dynamic signs. This work establish a foundational infrastructure for computational PSL applications that include text-to-sign translation systems, sign language recognition models, and educational platforms. PSL SignBank represents a critical advance towards accessibility, digital inclusion, and empowerment of Pakistan\u27s deaf community and it also provide a replicable framework for under-resourced sign language documentation globally

Automatic Number Plate Recognition Using Deep Learning Under Night time and Low-Illumination Conditions

Intelligent traffic management relies heavily on the recognition and localization of license plate numbers of moving vehicles, making it a critical task in this field. Numerous methods have been proposed to automate this procedure, utilizing computer vision and image processing algorithms to extract the number and characters from the detected license plate in surveillance photos and videos. However, these methods have primarily focused on daytime photographs and films, neglecting the challenges posed by difficult weather conditions or dim lighting settings. As a result, identifying the position of license plates and interpreting the characters from them remains an understudied area, particularly in low-light environments and night time photography. In response, we present a Night Time number plate detector and recognizer model in this paper. The model begins with a YOLOv5-based detector that has been trained to detect license plates in dark and hazy vehicle photos, generating a polygon bounding box around the number plate. The second phase of the process comprises an improvement module, where the retrieved picture of the license plate undergoes a variety of filters. Lastly, Easy OCR is employed to read the characters on the license plate. Our experimental results demonstrate that training the detector on dark and low illumination photographs, along with precise bounding box generation, significantly improves detection and recognition accuracy. Specifically, our model achieved a mAP score of 97%, highlighting the efficacy of our approach. In conclusion, our Night Time number plate detector and recognizer model represents a significant step forward in the recognition and localization of license plate numbers, particularly in low-light conditions. Our approach provides a powerful and effective tool for intelligent traffic management systems, and we believe that our results will pave the way for further research in this field

Distinguishing Human-Generated and AI-Generated Academic Writing: A Machine Learning Benchmark Study

The rapid adoption of large language models (LLMs) such as ChatGPT has raised critical questions about authorship, originality, and integrity in academic writing. Unlike conventional plagiarism testing tools, AI-generated or AI-rephrased text can preserve the original meaning and context of the text while modifying the writing style, making it challenging to detect using standard similarity checks. This study addresses this challenge by creating a domain-specific corpus of postgraduate-level academic texts. The corpus contains 22,520 samples, equally divided between human-written text and AI-rephrased text. All samples were preprocessed and represented using two common techniques: TF-IDF and Word2Vec. The dataset was evaluated using well-known machine learning and deep learning models, including Logistic Regression, Support Vector Machines, Recurrent Neural Networks, and transformer-based models BERT and T5. The results show that linear and sequential models provide low baseline performance, with accuracy between 50-54%. While BERT significantly outperforms the other models, achieving 83% precision along with a high recall rate. Confusion matrix analysis further shows that traditional models tend to overpredict AI authorship, whereas BERT demonstrates strong reliability in distinguishing between human-written and AI-generated text. The results show that transformer-based models are more effective for authorship verification in academic settings. They also emphasize the trade-offs among interpretability, computational cost, and predictive performance. In general, this study offers some important recommendations for the creation of credible, transparent, and domain-sensitive AI detectors for academia

Enhanced Vertebral Segmentation and Cobb angle Calculation using Advanced Instance Segmentation Techniques

Scoliosis, a spinal deformity affecting children and adolescents, is quantified using the Cobb angle. Traditional manual measurement by clinicians is time-consuming and subject to variability. This study introduces an automated method using the YOLACT++ instance segmentation model to detect vertebrae and calculate the Cobb angle on the SpineWeb dataset. By identifying the most tilted vertebrae using principal component analysis, our approach achieves a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.11%, outperforming previous segmentation-based methods. This demonstrates improved accuracy and reliability, with potential for clinical decision support. The source code and other details are available at github link. https://github.com/inzamam-ulhaq-collab/Cobb-angle-measurement-code.gi

AI Based Makeup Recommendation System: A Suitable AI Solution for Women

This paper describes the development of an AI-driven makeup recommendation application and how it was developed with the use of OpenCV and dlib to process the data on the back side and machine learning algorithms to execute a recommendation, and Flitter in order to operate the front-facing camera. The application gives unique recommendations to users depending on their face structure and their interests, to transform the way individuals shop makeup. The tool is more accessible to the art of cosmetics and utilizes AI to enable users to use it and enhance their natural beauty with confidence. It also uses sophisticated algorithms to detect facial characteristics like color of skin, shape of eyes, and color of lips to recommend appropriate cosmetic products and methods. This creative method is not only very simplifying to the make up process it also promotes creativity and self expression

Lightweight Texture-Based Classification of Huffaz Status from Structural MRI Using GLCM Correlation and Brodmann-Area VOIs

Previous neuroimaging studies suggest that intensive Quran memorization may be associated with structural brain differences, but simple and interpretable MRI-based biomarkers remain limited. This study investigated whether a single interpretable radiomics feature, gray-level co-occurrence matrix (GLCM) Correlation, extracted from predefined Brodmann Area (BA) volumes of interest (VOIs), is associated with Huffaz status.A cross-sectional case-control design was used involving 47 participants (23 Huffaz, 24 non-Huffaz). Structural MRI scans were pre-processed using a standard SPM pipeline to generate modulated, warped, and smoothed tissue-class images (smwc*.nii). Inferential analyses were restricted to five literature-driven candidate regions (BA22, BA24, BA32, BA40, and BA46) to reduce multiplicity. Best-subset logistic regression was performed with sex forced into all models and Bayesian Information Criterion (BIC) used for model selection. Bootstrap resampling was applied to assess feature-selection stability.BA46 GLCM Correlation emerged as the lowest-BIC predictor set. Bootstrap resampling showed higher selection stability for BA46 (approximately 0.68 selection frequency) than for the other candidate regions (approximately 0.13–0.15), supporting a reproducible single-region signal within the tested set. These findings suggest that BA46 texture organization, as captured by GLCM Correlation, may differentiate Huffaz from non-Huffaz after adjustment for sex.The modest sample size and lack of external validation limit generalizability. Nevertheless, the findings support the potential of an interpretable single-feature radiomics biomarker and motivate further validation in larger independent cohorts, as well as robustness analyses across preprocessing and discretization settings

A Two-Stage Noisy Pre-training and Fine-tuning Pipeline for Low-Resource Named Entity Recognition in Shahmukhi Punjabi

Named Entity Recognition (NER) for low-resource languages remains a critical challenge in natural language processing, particularly for scripts with limited annotated corpora. This paper addresses this challenge for Shahmukhi Punjabi, an underrepresented Perso-Arabic script used by millions in Pakistan. We propose a two-stage training pipeline that leverages a large-scale machine-labeled corpus generated by a Bagging-CRF ensemble to warm-start multilingual transformer models before fine-tuning on a small, gold-standard human-annotated dataset. We evaluate five state-of-the-art multilingual transformers, mBERT, XLM-R, mmBERT, RemBERT, and mDeBERTa-V3, under two experimental settings: (A) direct supervised fine-tuning on the human-labeled dataset, and (B) the proposed two-stage pipeline. The human-labeled dataset comprises 979 sentences and 25,221 tokens, while the larger machine-labeled corpus having 16,586 sentences and 336,502, both tokens covering 13 entity types. Experimental results demonstrate consistent improvements across all five models, mmBERT and RemBERT achieve the highest weighted F1 scores of 0.85 and 0.86 respectively. The most striking gains are observed for mDeBERTa-V3 (+0.21 F1, 39.6% relative) and XLM-R (+0.20 F1, 33.3% relative), demonstrating that the two-stage pipeline provides the greatest benefit to models with limited baseline performance on low-resource scripts. These results validate the effectiveness of noisy domain adaptation as a data augmentation strategy for low-resource NER in morphologically rich, right-to-left scripts

AI-Based Writing Assistants in Higher Education: Impacts on Student Critical Thinking and Academic Integrity

The integration of AI-based writing assistants—such as ChatGPT, Grammarly, and QuillBot—has rapidly transformed academic writing practices in higher education. These tools offer instant feedback on grammar, coherence, and structure, and can even generate entire essays or research drafts. While many students and educators recognize the benefits of improved writing fluency, efficiency, and personalized support, significant concerns persist regarding the potential erosion of critical thinking skills and threats to academic integrity. Research highlights a dual impact: AI tools can foster self-directed learning and support skill development when thoughtfully integrated, but overreliance may diminish independent reasoning, creativity, and ethical standards. The literature underscores the need for balanced integration strategies that promote responsible use, ethical awareness, and continued development of core academic competencies. This systematic review synthesizes findings from 50 peer-reviewed studies to examine whether AI-based writing assistants negatively impact student critical thinking and academic integrity in higher education. The review reveals a nuanced picture: strong evidence supports improvements in grammar accuracy and surface-level writing skills, while moderate evidence indicates risks to independent critical thinking, particularly among less experienced writers. Academic integrity concerns are significant, with frequent use associated with increased plagiarism risk absent ethical training. Structured integration with AI literacy education and clear institutional guidelines mitigates negative effects. The review concludes with recommendations for balanced implementation that harnesses benefits while safeguarding core academic competencies