Volume 13 | Issue 4 |

 Abstract

This paper examines how the Indian epic Ramayana is rewritten with the help of technology and writing in the digital medium. With the advancement of digital media, the ancient books like the Ramayana are converting into an engaging visual experience. Among other major digital versions of the story that are the focus of this study includes Ramayana: The Epic (2010), an animated movie that was directed by Chetan Desai, and Ramayana: Clash of Ram - an interactive video game. This research looks at how these digital versions do transformation constructs, captivates everyone and affects how a person understands culture. By using a qualitative method within the ambit of Digital Humanities, it deals with how-the differences in structure, themes, and characterization between traditional manuscripts and their digital adaptations were studied. Digital Humanities is a term for viewing how culture has been mediated and is creating the transformed tales. It examines how digital versions of canonical texts change the way they were previously perceived and dramatically reconceives their notion of authenticity and representation. The study also investigates how new technologies such as AI, VR, and AR disrupt the traditional storytelling form by making the experience much more immersive. However, these new technologies also ignite the issues of culture preservation or rather defining true stories. Digital Humanities, as such, explores how the Ramayana became digital. It investigates technology's interface with archaic writings and modern digital culture. It then ends by gauging the "good" and "bad" for digital storytelling and bystanders to ensure that epics like the Ramayana will still obtain importance as technology advances.

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

This paper explores the idea of first rate employment as defined via the global Labour organisation (ILO) and examines the function of exertions legal guidelines in promoting honest, secure, and equitable paintings environments. decent work, encompassing fair wages, place of job safety, social safety, and inclusivity, is quintessential to human properly-being, financial balance, and social concord. The paper in addition analyses how labor laws, which include minimum wage requirements, administrative center protection regulations, anti-discrimination measures, and social protection advantages, function important equipment in translating the standards of decent paintings into practice. The studies additionally delves into the challenges and opportunities associated with exertions laws, specially in light of the upward push of the gig economy and casual sectors. whilst exertions laws have proven achievement in sure areas like Scandinavia and Germany, issues consisting of susceptible enforcement, exclusion of casual workers, and cultural resistance continue to be widespread challenges. The paper concludes through emphasizing the want for adaptive hard work policies which could address evolving group of workers dynamics and make contributions to the achievement of worldwide sustainable development goals, ensuring that every one employees revel in decent paintings possibilities.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

first rate paintings, labor laws, place of job protection, Social security, Gig economy, Sustainable development desires.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Deepfake technology has advanced significantly, posing serious threats in the form of manipulated media, including synthetic audio that mimics real human voices. This project presents a Deepfake Audio Detection System that utilizes Mel-Frequency Cepstral Coefficients (MFCCs) as feature representations and a Support Vector Machine (SVM) classifier to differentiate between genuine and deepfake audio samples. The system extracts MFCC features from .wav files, scales them using Standard Scaler, and classifies them using a pre-trained SVM model. The Flask-based web interface allows users to upload audio files and receive real-time classification results. Experimental evaluation demonstrates the system's ability to effectively distinguish deepfake audio, contributing to enhanced digital media security.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fake Audio Identification, Digital Media Security, Audio Classification, Feature Extraction, Flask Web Application.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A hydrogel is a network of hydrophilic polymer chains, similar to a colloidal gel in which water is dispersed medium. Hydrogels, whether natural or manmade, are highly absorbent polymeric networks that can contain over 90% water. Because of their high water content, hydrogels are flexible and comparable to natural tissue. The literature on this topic is growing, particularly in scientific research fields. This paper aims to evaluate the literature on hydrogel classification, physical and chemical properties, preparation methods, and technological feasibility.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Hydrogel, colloidal gel, hydrophilic, Chemical cross-linking, Polymer, Application.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract The rapid adoption of digital payment systems has transformed financial transactions, enhancing convenience and efficiency. However, these systems are also vulnerable to various security threats, including fraud, phishing attacks, data breaches, and unauthorized transactions. This study aims to examine the security threats in digital payment systems within Malappuram District. Using a structured survey of 100 respondents, the research analyses the awareness and experiences of digital payment users regarding security risks. The findings highlight the most prevalent threats and suggest measures to enhance security in digital transactions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Payments, Security Threats, Cyber Fraud, Online Transactions

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Creative Commons Attribution 4.0 and The Open Definition

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In the Automotive Industry, dynamic pricing is used a lot to make the most money and hold off the competition. The Automotive industry is using AI to build a data-centric framework that will allow dynamic pricing. This research will look at how they are doing it. Automakers can find out about how customers act, how the market is changing, and how competitors plan to beat them by using complicated formulas and strict data collection methods. The aim of this research is to analyze how dynamic pricing protects prices in various industries, with a particular focus on its application in the automotive industry. In addition, the research will discuss about data-driven design approaches incorporating with artificial intelligence (AI), mainly how these technologies could be used to improve pricing strategies by automating choices and letting prices adjust based on the market. Important things like how to use market trends to our advantage, gather and analyze data, and understand how customers behave, and merchandise sales are the focus areas of the paper. As part of the project, AI could also be used to improve pricing methods. Some of these are prediction analytics, machine learning, and reinforcement learning. We can figure out how to make the most money and guess what prices will be in the future by using algorithms that look at past price data. Finally, the study shows that price strategies that are driven by AI and design that is driven by data can have a big impact on the automotive industry. Businesses in the Automotive industry might be able to boost competition, new ideas, and customer trust by using dynamic pricing systems and staying honest all the way through.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Dynamic pricing, Automotive industry, Data-driven architecture, AI algorithms, Pricing optimization, Consumer behavior, Ethical considerations, Regulatory compliance

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent years, there has been significant rise in the recognition and adaption of Artificial Intelligence and Machine Learning. Now A days Face age progression is becoming a widely used technique in the modern era as it serves numerous applications. Real-time face aging progression using Generative Adversarial Networks (GANs) is an advanced deep learning approach that enables the realistic simulation of facial aging and rejuvenation. Unlike traditional image processing methods, GANs ensure photorealistic results by synthesizing fine-grained details such as wrinkles, skin texture, and facial shape variations. The proposed system can be applied in various fields, including forensics, entertainment, and digital personalization. By implementing real-time processing approach, which ensures instant and seamless age transformation, making it suitable for interactive applications. The integration of advanced deep learning techniques, such as attention mechanisms and progressive training, further enhances accuracy and realism. This study explores the potential of GAN-based face aging progression, highlighting its effectiveness in generating lifelike aging transformations while addressing challenges such as identity preservation and dataset biases. In our project, we will explore and implement face progression over different ages. We will use some frameworks, Libraries in Deep Learning Networks and Python to implement our application. Using Generative Adversarial Networks, we seek to generate older versions of oneself while preserving the identity of the individual. Using Generative Adversarial Networks, we seek to generate older versions of oneself while preserving the identity of the individual.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face Aging, Generative Adversarial Networks (GAN), Real Time Image Processing, Age Progression, Deep Learning, Computer Vision.

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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

 Keywords

Digital competition act, Anticompetitive, Digital market, Ex-ante regulation, Systemically significant digital enterprises.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As internet shopping grows, so does demand for sustainable choices. The Mobile Shopping and environmentally friendly Swapping web application combines mobile purchasing with a durable trade-in mechanism. Buying new and refurbished things and swapping gently used devices, books, and clothing reduces waste and promotes a circular economy. The platform offers personalised recommendations, secure payment mechanisms, and real-time inventory adjustments to improve user experience. It makes sure that traded-in products are ethically recycled or reconditioned by working with recycling facilities including environmental NGOs. The site awards eco-friendly purchases to encourage sustainable spending. This concept aims to easily integrate sustainability into online shopping. The platform integrates technology with ethical consumerism to facilitate convenient and eco-friendly purchase. This strategy emphasises simplicity, long-term viability and scalability to establish a new benchmark for e-commerce.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Environmentally sustainable transactions, green finance, sustainable purchasing, mobile commerce, secure exchanges, real-time inventory management, AI-augmented procurement, waste reduction, responsible recycling, environmentally aware choices, incentive-driven rewards.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Over the past few years, AI has revolutionized the creation of ultra-realistic face-swapped images, known as deepfakes. These images have become so convincing that they have often been nearly impossible to detect with the naked eye. While deepfake technology has been used for fun and entertainment, it has also had a dark side. It has spread political misinformation, been used for blackmail, and even created fake news about major events, causing serious harm. To tackle this growing problem, researchers have developed deep learning models to detect deepfakes. In this paper, we have introduced LBPNET, a new model designed to identify fake images more accurately. Our approach has started by analysing the fine details in an image's texture using Local Binary Pattern (LBP) features. These features have helped capture tiny differences that have set real and fake images apart. We have then used a Convolutional Neural Network (CNN) to train the system so it has been able to recognize deepfakes with greater precision.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deepfakes , LBP , DEEP LEARNING

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Creative Commons Attribution 4.0 and The Open Definition