Volume 13 | Issue 10 |

 Abstract

This project introduces a real-time object detection and localization system that leverages the YOLO (You Only Look Once) pre-trained model, integrated with an intuitive user interface developed using Python's Tkinter framework. The system aims to enhance object recognition by delivering immediate visual feedback along with audio-based alerts, announcing both the identity and relative position of detected objects within the camera frame. Users interact with the system via a graphical interface that features a "Start Detection" button, initiating a live video stream. The YOLO model processes this stream in real time, identifying objects and estimating their spatial locations--such as left, right, top, bottom, or center. To support accessibility, particularly for visually impaired users or in hands-free environments, the system incorporates text-to-speech synthesis to provide verbal descriptions of the detections. By combining efficient object detection, spatial localization, and audio output, this system demonstrates practical utility in areas such as surveillance, assistive technologies, and industrial automation. The integration of YOLO ensures high-speed, accurate detection, offering a seamless and responsive user experience.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Index Terms- YOLO (you only look once), Tkinter, OpenCV, Text to Speech Algorithm (TTS).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The problem of predicting equity prices is fundamentally challenging, since the time series of the market are noisy and non-stationary, and influenced by nonlinear long-range processes. Standard models do not support these long-term dependencies. We use Long Short-Term Memory (LSTM) networks, which are an RNN architecture designed to operate over long horizon memories, to make price projections based on historical data. Our pipeline uses cautious feature selection, normalization, sequence windowing, and hyperparameters (units, learning rate, and dropout) are optimized by grid search. The model, trained over 100 epochs using separate training, validation, and test divisions, has a predictive accuracy of 98 percent on held-out data. These findings reaffirm the power of LSTM with regard to the ability to model complex time-structure and outperform rival methods, providing useful decision support to investors and analysts. Further developments might add real-time feeds and other financial indicators to give it even more real-world utility.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Stock price forecasting; Long Short-Memory (LSTM); Recurrent Neural Networks (RNN); Deep learning; Time-series analysis; Nonlinear dynamics; Long-range dependencies; Feature engineering; Data normalization; Sliding-window sequences; Hyperparameter optimization; Grid search; Dropout; Learning rate; Train-validation-test split; Evaluation metrics (MAE, RMSE, MAPE); Directional accuracy; Financial markets; Volatility modeling; Real-time data integration.

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Creative Commons Attribution 4.0 and The Open Definition

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This paper presents the design and implementation of a Smart CNG Reservation System that enables users to pre-book CNG fueling slots and allows pump owners to manage station availability, subject to administrative approval. The system is developed as a Java web application using JSP and Servlets with MySQL as the backend database. It includes three modules: user, pump-owner, and administrator, each with distinct functionalities such as slot booking, pump approval, and dashboard management. Additional features such as an integrated chatbot and location-based mapping improve usability. The system reduces waiting times at CNG stations, improves pump utilization efficiency, and demonstrates scalability for urban fuel management. Future enhancements include mobile app integration and predictive slot allocation using machine learning.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNG reservation, slot booking, JSP/Servlets, MySQL, smart fuel management, web application

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Non-Government Organisations (NGOs) play a critical role in empowering persons with disabilities and promoting their social, economic, and educational inclusion. Acting as a bridge between policy frameworks and ground-level implementation, NGOs provide a wide range of services, including education, vocational training, rehabilitation, healthcare, and advocacy. They help individuals develop skills, gain employment, and participate actively in social and civic life, while also addressing societal stigma and fostering inclusive communities. This paper examines the historical and legal context of disability rights in India and globally, highlighting the role of key legislations such as the RPwD Act, 2016, and the UNCRPD. It explores the types of NGOs, their approaches, and core functions, including awareness generation, policy advocacy, capacity building, and direct service delivery. Case studies illustrate successful interventions and best practices, while the challenges faced by NGOs--such as funding constraints, regulatory hurdles, and societal barriers--are discussed. The study emphasizes the impact of NGO initiatives on individual empowerment, community development, and systemic change. Recommendations for future directions focus on strengthening partnerships, leveraging technology, enhancing organizational capacity, and improving monitoring and evaluation. By highlighting the indispensable role of NGOs, this paper underscores their contribution to creating an inclusive society where persons with disabilities can exercise their rights fully, achieve independence, and participate meaningfully in all spheres of life.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Non-Government Organisations (NGOs), Social Inclusion, RPWD Act, 2016, UNCRPD, Vocational Training, Rehabilitation Services.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Face swapping has become an important area of research in computer vision. It is driven by uses in entertainment, privacy, virtual reality, and digital forensics. This review summarizes progress from ten recent studies focusing on GAN-based, diffusion-based, and hybrid models. These studies tackle issues like high fidelity, keeping identities intact, handling occlusions, pose invariance, and scalability. Early GAN-based models such as SimSwap, FaceShifter, and FaceDancer introduced new techniques like identity injection, adaptive embedding fusion, and occlusion-aware refinement. These methods aim to balance transferring the source identity with preserving target attributes. High-resolution techniques like MegaFS used hierarchical latent encoding and StyleGAN2 synthesis for megapixel swaps. In contrast, low-resolution and occlusion-focused methods applied cross-resolution contrastive learning and special inpainting pipelines. Diffusion-based techniques, including DiffSwap, DiffFace, and unified inpainting frameworks, have improved stability, control, and image realism. They achieved this through identity guided sampling, 3D-aware masking, and CLIP feature disentanglement. Together, these advancements show significant development in creating realistic, high-resolution, and attribute-consistent swapped faces. However, there are still challenges with extreme pose variation, detailed preservation, and real-time efficiency. This review discusses the strengths and weaknesses of these approaches and suggests future research directions for creating reliable, ethical, and controllable face swapping systems.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face swapping, identity preservation, attribute preservation, generative adversarial networks (GANs), diffusion models, high-fidelity synthesis, occlusion handling, pose invariance, hierarchical latent encoding, 3D-aware masking, inpainting, high-resolution image synthesis, real-time processing, controllable face editing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Personalized medicine is causing a paradigm shift in pharmaceutical technology by moving drug development away from the traditional "one-size-fits-all" approach and toward therapies tailored to each patient's specific needs. Advances in genomics, molecular diagnostics, and digital health have enabled the identification of genetic variants, biomarkers, and disease pathways that impact the safety and effectiveness of medications. By applying these insights to pharmaceutical technology, researchers can develop new biopharmaceuticals, companion diagnostics, and targeted drug delivery systems that maximize therapeutic efficacy while minimizing side effects. To make customized medicine a reality, pharmaceutical innovation is crucial. Site-specific release is made possible by nanotechnology-based delivery systems, and RNA-based therapies and monoclonal antibodies are excellent illustrations of the precision that can be attained with modern drug development. Artificial intelligence and machine learning enhance predictive modeling, enable adaptive clinical trial designs, and expedite drug repurposing to reduce expenses and development delays. Therapy paradigms in neurology, cardiology, cancer, and rare diseases are already being altered by personalized approaches. Despite its potential, personalized medicine faces challenges. Expensive costs, intricate regulations, and unequal access limit adoption. The use of genetic data still raises ethical and privacy concerns. Additionally, healthcare systems need to adapt to incorporate advanced diagnostics, personalized treatment plans, and real-time patient monitoring into clinical procedures. The convergence of digital health, genetics, bioinformatics, and pharmaceutical technology will create unprecedented opportunities in the future. The management of diseases could be drastically changed by gene editing technologies like CRISPR, immunotherapies, and personalized vaccinations. Collaboration between academic institutions, industry, and regulatory agencies will be necessary to fully realize the transformative potential of personalized medicine and ensure equitable access and improved outcomes for a range of patient populations.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Personalized medicine, pharmaceutical technology, drug development, pharmacogenomics, nanomedicine, individualized therapy, biomarkers.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A study to evaluate the influence of comprehensive pre-operative nursing interventions on post-operative outcomes in patients undergoing cardiac surgery at a selected hospital.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cardiovascular Disease, Cardiac Surgery

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The forecasting of stock market trends has advanced with a sequence of paradigms from statistical schemes, machine learning algorithms, and deep learning architectures; nevertheless, issues remain with the effective representation of seasonal patterns and external influences. This research examines the ways in which current schemes handle cycles of a periodic kind--directly with the help of decomposition or indirectly with the use of deep architectures--and the importance of external factors like sentiment analysis, macroeconomic indicators, and time-based indicators. The notable finding is the disconnected form of these schemes, wherein the seasonal factors and external influences are often examined independently. The discussion integrates these findings and highlights the need for unified schemes wherein seasonality-aware modeling may combine with nonlinear learning schemes and structured external inputs. As a next step, a seasonal-aware framework with external integration is outlined.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Stock market prediction, Seasonality, Time series forecasting, Exogenous features, Sentiment analysis, Deep learning.

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Creative Commons Attribution 4.0 and The Open Definition