Volume 13 | Issue 4 |

 Abstract

?????- ???? ???? ??????? ??? ????? ?? ???? ??? ?? ?????? ?? ?????? ???????? ?? ??? ??? ????? ??????? ??? ??????? ??? ??? ?? ???????? ?? ?? ?? ?????????? ??????? ?? ?? ?? ???? ???? ?? ??????? ???? ??? ????????? ?????? ?? ?? ????? ???? ??? ???? ????????? ?????? ????? ??? ???? ?? ??????? ??????? ??? ??? ???? ???????? ?????? ?????? ?????????? ?????? ?? ?? ??????? ?? ???? ?? ???? ??? ?????????? ?????? ?? ??? ???? ??????? ???? ???????? ??? ????????? ??? ????????? ???????? ?? ????? ??? ??? ???? ???????? ??? ??? ????? ?? ??? ?? ??? ??? ??? ?????? ??? ???????? ??? ??????? ???? ?????? ?? ?????? ??????, ?????? ?? ????? ?????? ?????? ????? ??? ????? ???? ???? ???? ????? ?????? ????? ????? ??? ????? ?? ??????-?????????????????? ????????? ???? ??? ??? ?? ???? ??? ??????????? ?? ??? ???? ??? ?? ?? ???? ??? ????? ???? ???? ?? ??? ???? ???????? ??????? ??? ??? ??????? ???? ??? ???????? - ??? ???? ?? ????? ???????? ???? ?????? ?? ?????? ??????, ?????? ??? ????? ?????? ?? ?????????? ????? ???? ??? ????? ?? ????? ????????? ?? ???-??? ??????????? ??? ??????? ??? ??????? ??????? ?? ???????? ???? ??? ??????? - ??? ?? ?????????? ?? ???????? ???? ??? ????????? ??? ??????? ?? ???????? ?????? ????? ???????? ??? ??? ?????? ???? ??? ??? ????????? ??? ??????? ?? ??????? ?????? ?? ?????? ?? ???????? ????? ?????? ?? ????????? ?????? ?? ???? ?? ????? ?? ???? ????????? ????? ???????? ?? ????? ????????? ???? ??? ??? ?????? ??? ???????? - ??????? ??? ????? ??? ??????? ??????, ?????? ?? ????? ?????? ?????? ???????? ????? ????? ??? ?? ????? ?? ??? ?? ??????? ??????? ?????? ???? ???? ???? ?? ??? ?? ???? ?? ???? ??????? ??? ??????? ?????? ?????? ??? ?? ??????? ????, ???? ?? ????? ???? ?? ????? ?? ???? ???? ???? ??? ???????? - ?????????, ???????, ?????????? ??? ????????? ?? ???? ???? ?????? ?? ????????? ????? ??? ????? ?? ???? ???? ?? ???? ??? ???? ?? ???????? ??? ???

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Creative Commons Attribution 4.0 and The Open Definition

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???? ??? ?????, ??????-??????????????????, ????

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Creative Commons Attribution 4.0 and The Open Definition

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Automatic video summarization is an essential solution to the rapidly growing volume of digital video content, enabling efficient information retrieval and content management. This project introduces a deep learning-based approach that combines multiple advanced architectures to generate context-aware and user-specific video summaries. TransNetV2 is used for precise shot segmentation, while a Convolutional Neural Network (CNN) extracts abstract visual features. DBSCAN clustering filters redundant frames, and CLIP enhances content understanding by aligning visual and textual information. The system processes both visual and audio data and generates concise summaries tailored to user preferences. Implemented as a Flask web application, it allows users to upload videos or provide YouTube URLs for summarization. Evaluation results demonstrate improved performance in keyframe selection, with higher precision, recall, and mean Average Precision (MAP), while significantly reducing video length without losing essential content. The system holds potential for enhancing content recommendation, discovery, and digital media organization across domains like education, journalism, and research. Future work aims to support real-time summarization and multilingual capabilities.

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 Keywords

Deep learning, Video summarization, CNN, TransNetV2, CLIP, DBSCAN, Flask Web Application, Audio-visual processing.

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Creative Commons Attribution 4.0 and The Open Definition

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With rapid urbanization and the exponential growth of vehicular traffic, traditional traffic management systems struggle with increasing congestion, inefficiency, and environmental impact. Intelligent Traffic Signaling (ITS) systems offer a solution by utilizing real-time data analysis, machine learning algorithms, and Internet of Things (IoT) devices to optimize traffic flow and reduce delays. These systems monitor key parameters like vehicle density, speed, and pedestrian activity using sensors, cameras, and connected vehicles, dynamically adjusting signal timings to improve traffic distribution. Advanced algorithms, such as reinforcement learning, enable ITS to continuously adapt based on historical traffic patterns, seasonal variations, and special events. By reducing unnecessary idling and stop-and-go driving, ITS also lowers fuel consumption and emissions. As cities evolve toward smart infrastructure, ITS plays a key role in reducing traffic accidents, enhancing road safety, and improving urban mobility.

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 Keywords

Dynamic Signal timings, Intelligent Traffic Signalling(ITS), Reduce Delays, Fuel Consumption, Rapid Urbanization, Optimize traffic flow, Advanced algorithms.

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Creative Commons Attribution 4.0 and The Open Definition

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This research paper explores the development, functionality, and impact of RealAR, an advanced real estate platform integrating 3D model visualization and augmented reality (AR) to enhance property showcasing and buyer engagement. The study adopts a user-centered approach, combining quantitative analysis of user interactions with qualitative evaluation of the platform's AR capabilities, interactive UI, and real-time communication features. Through systematic evaluation, the research assesses the effectiveness of 3D model-based property exploration, real-time chat functionalities, and advanced filtering mechanisms in improving user decision-making and engagement. The findings underscore the importance of integrating WebAR, dynamic property listings, and real-time interaction in creating an immersive and informed propertybuying experience. Additionally, the paper examines the implications of interactive 360-degree views, AI-powered recommendations, and seamless navigation on user satisfaction and adoption rates.

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Creative Commons Attribution 4.0 and The Open Definition

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Trading, Market, Educative

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Creative Commons Attribution 4.0 and The Open Definition

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India's foreign policy has entered a challenging phase of strategic recalibration as global power dynamics evolve. The country must strike a difficult balance between its long-standing defence and energy relations with Russia and its growing technological, economic, and security ties with the US. This paper critically investigates how India has manoeuvred its diplomatic stance to assure military readiness, economic growth, and regional stability in the context of the Ukraine conflict, rising US-China tensions, and global trade realignments. The study examines India's strategic autonomy, defence modernisation, and involvement in multilateral forums using a qualitative analysis of secondary sources, revealing that India's decisions are primarily motivated by pragmatic national interests.

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 Keywords

India-US Relations, India-Russia Relations, Strategic Autonomy, Defense Diplomacy, Ukraine War. U.S.-China Rivalry, Indo- Pacific, Geopolitics

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Creative Commons Attribution 4.0 and The Open Definition

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Miniature painting in India originated from the Buddhist manuscript Illustrations in the Pala period of Nepal and the Eastern side of India in the 8th to 11th century. Miniature art thrived from the early 16th century until the mid-19th century. The notion of portraiture, or the accurate representation of humans, originated in Western art. Its application in Mughal painting started in the late 16th century. However, the Mughal Empire in India added a level of elegance, refinement, and grace to this style of painting. With the fall of Mughal patronage, a significant number of artists relocated to the Punjab Hills. The Sikh School, which prospered under the guidance of Maharaja Ranjit Singh, had many different sites, and most of the paintings featured the life of Guru Nanak Dev and other Sikh Gurus.

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Sikh Miniature, Portraits, Spiritual, Simplicity, Janamsakhi.

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Creative Commons Attribution 4.0 and The Open Definition

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We have employed first-principles density functional theory (DFT) calculations to systematically explore the influence of partial substitution of Fe with Mg in FeAl?O? on its structural properties. This strategic substitution of Mg emerges as a crucial factor in enhancing the multifunctional characteristics of the material, particularly for advanced thermocatalytic applications. Despite its promising potential, the effect of Mg incorporation on FeAl?O? has remained unexplored from a first-principles perspective, especially in the context of solar thermochemical fuel generation and photovoltaic technologies. Our electronic density of states (DOS) analysis reveals a significant band gap upon Mg doping, when applied GGA there is no band gap and when we apply GGA+U there is band gap with 2.0 eV. Which in turn strongly modulates the electronic structure of the material making it a more efficient candidate for energy harvesting and conversion applications.

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Density Functional Theory, Electronic property, GGA+U

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Creative Commons Attribution 4.0 and The Open Definition

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: Plastic waste scattered in public spaces such as streets, parks, and industrial areas poses a major environmental concern. Traditional waste collection methods are labor-intensive, time-consuming, and often ineffective in covering large areas. To address this issue, an intelligent robotic system is proposed for the detection and collection of plastic bottle waste. The system utilizes a Raspberry Pi to control the robot and process input data. For object detection, a deep learning model, YOLOv8, based on Convolutional Neural Networks , is employed to identify and classify plastic bottles in real-time camera footage. The model is trained on a curated dataset of plastic waste images to ensure reliable detection. Once a plastic bottle is identified, the robot uses a mechanical arm to pick it up and place it into a collection bin. This system enhances the efficiency and safety of waste collection, particularly in hard-to-reach or hazardous areas. By reducing human involvement and automating the process, the proposed solution contributes to a cleaner, smarter, and more sustainable environment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Robotic Waste Collection, Waste Detection, Image Processing, Raspberry Pi Camera, OpenCV, Real-time Waste Monitoring, Deep Learning, Smart Waste Management, Autonomous Robot, Efficiency and Cleanliness, Vision-Based System.

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Creative Commons Attribution 4.0 and The Open Definition

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This study works towards the relationship of using AI (artificial intelligence), namely AI-PR(AI-based personalized recommendations), on consumer green product awareness (GPA), aimed at the online shopping is used in this study. Although AI-driven technologies are changing the way products are interacted with by consumers, their impact on green buying behavior is unknown. As it was recognized that human cognitive and emotional factors may affect AI's ability to promote sustainable choices, this study included emotional intelligence (EI) as a moderating variable. Based on this, the research aims to dig deeper into the role of AI upon these compounds (e.g. AI, AI-PR and EI) on environmental consciousness. The descriptive research method is used to study under which conditions data was collected using self self-administered survey to 403 respondents. The descriptive approach renders various dependence relationships among AI-based recommendations, emotional intelligence and consumers' knowledge on green products, available for study. The study achieves this by conducting a class-based statistical summary and comparative analysis of the behavioral tendencies that make AI effective in dictating how consumers can make sustainable choices. These findings suggest that AI-based and AI usage make an important contribution to increasing the green product awareness of consumers. However, emotional intelligence moderates such a relationship. Secondly, individuals with higher emotional intelligence have a stronger positive correlation between perceived AI usage and GPA, namely, emotionally intelligent consumers are more willing to accept the selling of their sustainable product through AI messaging. Conversely, emotional intelligence negatively moderates the relationship between AI-PR and GPA, meaning that highly emotionally intelligent individuals may be less influenced by AI-based personalized recommendations, whereas those with lower EI show a stronger response to AI-driven suggestions. Furthermore, this study contributes towards growing discourse on the intersection of AI, consumer behavior, and sustainability, highlighting the need for a balanced approach that integrates technology with human cognitive factors to drive positive environmental change.

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Keywords: AI-personalized recommendations, Emotional Intelligence, Green Product awareness, Green behavioural intentions, Mediated moderation.

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Creative Commons Attribution 4.0 and The Open Definition

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Bone fractures are a significant medical concern requiring accurate and timely diagnosis to ensure effective treatment. This paper presents a deep learning-based system for bone fracture detection and classification using Convolutional Neural Networks (CNNs). The system integrates a user-friendly Graphical User Interface (GUI) to facilitate the input of bone fracture images. The process begins with the selection of a bone fracture image, followed by image resizing for uniformity. A pre-processed dataset of bone fracture images is utilized for training the CNN model to detect and classify fractures into three categories: Mild, Moderate, and Severe. The deep learning model leverages advanced CNN architectures for feature extraction and classification, achieving high accuracy in predicting fracture severity. The GUI enables users to input images, run the detection process, and view classified outputs seamlessly. This automated system demonstrates the potential to assist healthcare professionals in diagnosing fractures quickly and accurately, reducing dependency on manual assessments and enhancing clinical decision-making. The achieved accuracy underscores the effectiveness of the proposed approach, making it a valuable tool in medical imaging applications and orthopaedic care.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bone Fracture images Dataset, Deep Learning algorithm, Convolutional Neural Network, GUI and Accuracy.

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Creative Commons Attribution 4.0 and The Open Definition