This research discusses the critical need for early detection and treatment for early prediction of Freezing of Gaits (FOG) utilizing a wearable sensor technology powered with LoRa communication. The system consisted of an Esp-32 microcontroller, in which the trained model is utilized utilizing the Micromlgen Python library. The research investigates accurate FOG classification based on pertinent clinical data by utilizing machine learning (ML) algorithms like Catboost, XGBoost, and Extra Tree classifiers. The XGBoost could classify with approximately 97% accuracy, along with 96% for the catboost and 90% for the Extra Trees Classifier model. The SHAP analysis interpretability shows that GYR SI degree is the most affecting factor in the prediction of the diseases. These results show the possibility of monitoring and identifying the affected person with tracking location on GPS and providing aid as an assistive technology for aiding the affected. The developed sensor-based technology has great potential for real-world problem solving in the field of healthcare and biomedical technology enhancements.

Sign languages serve as essential communication systems for individuals with hearing and speech impairments. However, digital linguistic dataset resources for underrepresented sign languages, such as Nepali Sign Language (NSL), remain scarce. This study introduces the first benchmark dataset for NSL, consisting of 36 gesture classes with 1,500 samples per class, designed to capture the structural and visual features of the language. To evaluate recognition performance, we fine-tuned MobileNetV2 and ResNet50 architectures on the dataset, achieving classification accuracies of 90.45% and 88.78%, respectively. These findings demonstrate the effectiveness of convolutional neural networks in sign recognition tasks, particularly within low-resource settings. To the best of our knowledge, this work represents the first systematic effort to construct a benchmark dataset and assess deep learning approaches for NSL recognition, highlighting the potential of transfer learning and fine-tuning for advancing research in underexplored sign languages.

This paper presents an Artificial Intelligence (AI) integrated novel approach to Brain - Computer Interface (BCI) - based wheelchair development, utilizing a motor imagery r ight - l eft - h and m ovement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left - hand movements using electroencephalogram (EEG) data. A pre - filtered dataset, obtained from an open - source EEG repository, was seg mented into arrays of 19x200 to capture the onset of hand movements. Th e data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter - based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a BiLSTM - BiGRU model that shows a superior test accuracy of 92. 26 % as compared with v arious machine learning baseline models, including XGBoost, EEGNet, and a transformer - based model . The Bi - LSTM - BiGRU attention - based model achieved a mean accuracy of 90.13 % through cross - validation, showcasing the potential of attention mechanisms in BCI applications. Keywords: Brain Computer Interface (BCI), BiLSTM - BiGRU, Raspberry Pi, E lectroencephalogram (EEG), Hybrid Deep learning 1. Introduction Brain - Computer Interfaces (BCIs) are advanced systems that establish direct communication between the human brain and external devices . In recent years, BCIs have been widely investigated for their potential to assist individuals with mobility impairments, offering novel pathways for restoring autonomy. This paper proposes a BCI - based wheelchair control system driven by electroencephalogra phy (EEG) signals associated with motor imagery. The proposed framework incorporates a variety of machine learning models with tailored hyperparameter optimization techniques, culminating in the deployment of a BiLSTM - BiGRU hybrid deep learning model for effective EEG signal classification.

Link prediction in knowledge graphs requires integrating structural information and semantic context to infer missing entities. While large language models offer strong generative reasoning capabilities, their limited exploitation of structural signals often results in structural sparsity and semantic ambiguity, especially under incomplete or zero-shot settings. To address these challenges, we propose SLiNT (Structure-aware Language model with Injection and coNtrastive Training), a modular framework that injects knowledge-graph-derived structural context into a frozen LLM backbone with lightweight LoRA-based adaptation for robust link prediction. Specifically, Structure-Guided Neighborhood Enhancement (SGNE) retrieves pseudo-neighbors to enrich sparse entities and mitigate missing context; Dynamic Hard Contrastive Learning (DHCL) introduces fine-grained supervision by interpolating hard positives and negatives to resolve entity-level ambiguity; and Gradient-Decoupled Dual Injection (GDDI) performs token-level structure-aware intervention while preserving the core LLM parameters. Experiments on WN18RR and FB15k-237 show that SLiNT achieves superior or competitive performance compared with both embedding-based and generation-based baselines, demonstrating the effectiveness of structure-aware representation learning for scalable knowledge graph completion.

Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets. Conventional training via back-propagation (BP) with losses like Mean Squared Error or Cross-Entropy often requires extensive iterations and may converge sub-optimally. Quantum computing offers a promising alternative by leveraging superposition, tunneling, and entanglement to search complex optimization landscapes more efficiently. In this work, we propose a hybrid optimization method that combines an Unconstrained Binary Quadratic Programming (UBQP) formulation with Stochastic Gradient Descent (SGD) to accelerate CNN training. Evaluated on the MNIST dataset, our approach achieves a 10--15\% accuracy improvement over a standard BP-CNN baseline while maintaining similar execution times. These results illustrate the potential of hybrid quantum-classical techniques in High-Performance Computing (HPC) environments for Big Data and Deep Learning. Fully realizing these benefits, however, requires a careful alignment of algorithmic structures with underlying quantum mechanisms.

Abstract: This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair navigation. Five different models were trained on a pre-filtered dataset that was divided into fixed-length windows using a sliding window technique. Each window contained statistical measurements, FFT coefficients for different frequency bands, and a label identifying the activity carried out during that window that was taken from an open-source Kaggle repository. The XGBoost model outperformed the other models, CatBoost, GRU, SVC, and XGBoost, with an accuracy of 60%. The CatBoost model with a major difference between training and testing accuracy shows overfitting, and similarly, the bestperforming model, with SVC, was implemented in a tkinter GUI. The wheelchair movement could be simulated in various directions, and a Raspberry Pi-powered wheelchair system for braincomputer interface is proposed here. Keywords: Brain Computer Interfacing, FFT (Fast Fourier Transform), Raspberry-pi, electroencephalogram 1. Introduction Brain-Computer Interfaces (BCIs) represent a cutting-edge technology that facilitates direct communication between the human brain and external devices. In recent years, BCIs have been widely explored for assisting individuals with mobility impairments. This paper focuses on a novel BCI-based wheelchair control system that leverages EEG signals associated with control using various movements related dataset. The system incorporates various machine learning models with various optimization techniques for hyper-parameter tuning and finally, shows an attention mechanism for enhancing the performance of Bi-directional Long Short-Term Memory (Bi-LSTM) networks, which are employed for EEG signal classification. To integrate the braincomputer interface (BCI) for the wheelchair, an analysis of brain activity is necessary-based on modern technology. The signs of brain activity can be obtained using a variety of techniques [1]. In order to help people with severe disabilities live their daily lives, new aids, gadgets, and assistive technologies are required, as demonstrated by the pandemic emergency of the coronavirus illness 2019 (COVID-19). Brain-Computer Interfaces (BCIs) that use electroencephalography (EEG) can help people who experience major health issues become more independent and participate in activities more easily. This can improve their general well-being and prevent deficits [2].

Automating brain tumor segmentation using deep learning methods is an ongoing challenge in medical imaging. Multiple lingering issues exist including domain-shift and applications in low-resource settings which brings a unique set of challenges including scarcity of data. As a step towards solving these specific problems, we propose Convolutional adapter-inspired Parameter-efficient Fine-tuning (PEFT) of MedNeXt architecture. To validate our idea, we show our method performs comparable to full fine-tuning with the added benefit of reduced training compute using BraTS-2021 as pre-training dataset and BraTS-Africa as the fine-tuning dataset. BraTS-Africa consists of a small dataset (60 train / 35 validation) from the Sub-Saharan African population with marked shift in the MRI quality compared to BraTS-2021 (1251 train samples). We first show that models trained on BraTS-2021 dataset do not generalize well to BraTS-Africa as shown by 20% reduction in mean dice on BraTS-Africa validation samples. Then, we show that PEFT can leverage both the BraTS-2021 and BraTS-Africa dataset to obtain mean dice of 0.8 compared to 0.72 when trained only on BraTS-Africa. Finally, We show that PEFT (0.80 mean dice) results in comparable performance to full fine-tuning (0.77 mean dice) which may show PEFT to be better on average but the boxplots show that full finetuning results is much lesser variance in performance. Nevertheless, on disaggregation of the dice metrics, we find that the model has tendency to oversegment as shown by high specificity (0.99) compared to relatively low sensitivity(0.75). The source code is available at https://github.com/CAMERA-MRI/SPARK2024/tree/main/PEFT_MedNeXt

Multi-modal deep metric learning is crucial for effectively capturing diverse representations in tasks such as face verification, fine-grained object recognition, and product search. Traditional approaches to metric learning, whether based on distance or margin metrics, primarily emphasize class separation, often overlooking the intra-class distribution essential for multi-modal feature learning. In this context, we propose a novel loss function called Density-Aware Adaptive Margin Loss(DAAL), which preserves the density distribution of embeddings while encouraging the formation of adaptive sub-clusters within each class. By employing an adaptive line strategy, DAAL not only enhances intra-class variance but also ensures robust inter-class separation, facilitating effective multi-modal representation. Comprehensive experiments on benchmark fine-grained datasets demonstrate the superior performance of DAAL, underscoring its potential in advancing retrieval applications and multi-modal deep metric learning.

Transforming text-based identity documents, such as Nepali citizenship cards, into a structured digital format poses several challenges due to the distinct characteristics of the Nepali script and minor variations in print alignment and contrast across different cards. This work proposes a robust system using YOLOv8 for accurate text object detection and an OCR algorithm based on Optimized PyTesseract. The system, implemented within the context of a mobile application, allows for the automated extraction of important textual information from both the front and the back side of Nepali citizenship cards, including names, citizenship numbers, and dates of birth. The final YOLOv8 model was accurate, with a mean average precision of 99.1% for text detection on the front and 96.1% on the back. The tested PyTesseract optimized for Nepali characters outperformed the standard OCR regarding flexibility and accuracy, extracting text from images with clean and noisy backgrounds and various contrasts. Using preprocessing steps such as converting the images into grayscale, removing noise from the images, and detecting edges further improved the system's OCR accuracy, even for low-quality photos. This work expands the current body of research in multilingual OCR and document analysis, especially for low-resource languages such as Nepali. It emphasizes the effectiveness of combining the latest object detection framework with OCR models that have been fine-tuned for practical applications.

The combination of large language models (LLMs), augmented reality (AR), and user interface/user experience (UI/UX) design in therapies for children, especially with disorders like autism spectrum disorder (ASD), is examined in this review study. Three primary areas are covered in this review: how AR can improve social and learning results; how LLMs can help with communication; and how UI/UX design affects how effective these technologies are. Results reveal that while LLMs can provide individualized learning and communication support, AR has demonstrated promise in enhancing social skills, motivation, and attention. For children with ASD, accessible and interesting interventions depend heavily on effective UI/UX design. To optimize the benefits of these technologies in ASD therapies, the study emphasizes the need for additional research to address difficulties related to customization, accessibility, and integration. Keywords: Autism Spectrum Disorder, Large Language Models (LLM), Augmented Reality (AR), Virtual Reality (VR) 1. Introduction Children with autism can benefit greatly from digitally assisted language therapies thanks to augmented reality (AR). Numerous results and insights about the use of augmented reality (AR) as a teaching and pedagogical aid have been reported by educators and researchers [1]. The use of computer technology--particularly augmented reality--in autism spectrum disorder (ASD) therapies has grown as a means of treating or mitigating the symptoms of the disorder. Not just for kids of a certain age or educational level, augmented reality is an entertaining form of technology that facilitates easy interaction and helps kids comprehend and retain information [2]. A neurodevelopmental disorder known as autism spectrum disorder (ASD) is marked by recurring problems with social interaction and communication, as well as a limitation in interests and repetitive activities [3]. It is believed that one in every 100 youngsters worldwide is affected by ASD.