Volume 13 | Issue 4 |

 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.

  License

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

 Abstract

The Comprehensive Career Advancement Interface is a robust, all-in-one platform designed to support individuals in achieving long-term career growth. It combines job search functionality, skill development tools, and professional networking features to offer a complete career support system. Leveraging artificial intelligence, machine learning, and data analytics, the platform creates personalized career pathways based on each user's skills, experience, and current industry trends. A key feature is its ability to assess users' career progress, identify skill gaps, and recommend relevant courses, certifications, and training programs. It also facilitates mentorship and networking with industry professionals, allowing users to gain valuable career insights and opportunities. Security and privacy are strongly emphasized, with advanced encryption and authentication systems in place to protect personal data. Additionally, the interface provides intelligent feedback, interview guidance, and career coaching based on user activity. Continuously updated to reflect market demands, this innovative platform empowers users to stay competitive and successfully navigate their career journeys across various industries.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Career advancement, career development, job search, skills assessment, networking.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The research analyzes how mergers and acquisitions (M&A) affect labour rights in the Indian banking sector. The research explores legal challenges and issues that occur from complex transactions in the Indian banking sector transactions. The study examines the key legislative frameworks including the Industrial Disputes Act, 1947, which specifies rules about transfer of employment and compensation; the Contract Labour (Regulation and Abolition) Act, 1970 which discusses implications for contract workers in M& A scenarios and the Payment of Gratuity Act, 1972 which highlights the challenges in safeguarding employee entitlements during mergers. Additionally, the paper evaluates, The Reserve Bank of India guidelines to demonstrate banking sector applications of regulatory requirements and employee protection. Through a comprehensive analysis of various M&A schemes within the banking sector, the efficacy of these frameworks is assessed, drawing on case laws that illustrate judicial interpretations and outcomes related to employees' rights during the mergers. The data collected from the employees regarding M&A effects is analysed to provide insights into the real world and practical implications of these legal provisions. The study focuses on key challenges, including job security, compensation disputes and the adequacy of existing labour laws in addressing these M&As in the Banking sector. By synthesizing legal analysis with these data, this paper aims to contribute to a deeper understanding of labour rights protection with respect to Banking mergers and acquisitions in India and gives recommendations for enhancing the legal framework to better safeguard employee interests during these transformative processes.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

mergers and acquisitions, labour rights, schemes, employees, amalgamation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

ABSTRACT: The fashion industry is the world's second-largest generators of textile waste, making it a significant contributor to global waste. The regular production of new textiles and the huge quantity of garbage that is regularly dumped in landfills are the main causes of this ongoing issue. The project, titled "Sustainable Apparel Development ", tries to address this issue by applying textile materials or discarded waste fabrics to make laptop bags that are both visually beautiful and useful. This project's main goal is to use upcycling as a means of promoting sustainability and trash reduction. by turning what are normally regarded as waste fabric remnants into useful goods. Gathering used textiles, carefully sifting and choosing appropriate pieces, and designing an inventive and useful bag are all steps in the project process. The upcycling project not only improves the environment but also promotes the value of social responsibility. The project supports the development and enhancement of pattern making--which are often digitalized in today's fast-paced, highly technological world--by working with care and creativity. This combination ensures that there is a second chance of life to the discarded scarps.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Upcycling Fashion, Textile Waste, Sustainable Apparel, DIY Clothing, Reclaimed Fabrics.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The NLP-Based Personalized Craft Idea Generator is an advanced AI-driven system that offers customized craft ideas tailored to individual preferences, including skill level, available materials, specific occasions, and sustainability considerations. By leveraging cutting-edge natural language processing (NLP) models such as T5 and GPT, the system efficiently processes user inputs to generate well-structured and innovative craft suggestions. The platform integrates technologies like React.js for the frontend, MongoDB for data management, and NLP libraries to ensure smooth functionality. Designed for hobbyists, educators, and DIY enthusiasts, it simplifies the creative process by providing personalized recommendations, reducing the need for extensive manual searches. Additionally, the system promotes eco-friendly crafting by encouraging the use of sustainable materials. Future developments aim to enhance user interaction through voice-based inputs and real-time AI-driven personalization, making crafting more intuitive, accessible, and inspiring for users of all experience levels.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

T5 Model,GPT Models, Recommendation System, Natural Language Processing (NLP), Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The categorization of sleep disorders has proven to be of main importance in the advancement of human quality of life, due to the fact that sleep apnea and other sleep disorders affect the general health of a person. Sleep stage analysis is inherently difficult and vulnerable to human errors, meaning that great need lies in the development of strong machine learning algorithms for an effective classification of sleep disorders. The paper discusses a comparative study of conventional machine learning algorithms with for classifying sleep disorders using the publicly available Sleep Health and Lifestyle Dataset that contains 400 rows and 13 columns representing various features related to sleep and daily activities. Several parameters of different MLAs are optimized in the paper using a genetic algorithm to improve classification performance. The performance of the k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression algorithms were tested alongside XGBoost for an all-around check on their correctness. Experimental results show that classification accuracy of with PCA showed 94.44%, 93.06%, 94.44%, 94.44%, 93.06%, and 94.44%, on applying PCA showed result 95.83%, 94.44%, 94.44%, 94.44%, 94.44%, and 94.44% respectively. The Logistic Regression achieved the highest classification accuracy of 95.83%, and its precision, recall and F1-score values on the testing data were 94.50%, 94.44% and 94.44%, respectively. The findings of the study increase the efficiency of the proposed algorithms in classifying sleep disorder. It contributes to the efforts that have been made in enhancing the accuracy of diagnostics. This integrated review declares the immense potential of machine learning in creating high-quality, automatic sleep health solutions, which serve as doorsteps to future innovation in sleep disorder management.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sleep disorders, Machine learning, Classification, Sleep stages, Diagnosis, Prediction, Sleep apnea, Sleep quality, Sleep patterns.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Amarkant Ke Upanyaso Me Bhartiya Pariwar Ka Swaroop

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

8218194425

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The concept of Indian identity as portrayed in Indian writing in English, explores the intricate ways in which Indian writers navigate cultural, historical, and social contexts to express a multifaceted sense of self. It examines how Indian authors use English as a medium to bridge the gap between traditional Indian values and the globalized world, while reflecting on the complexities of identity formation in post-colonial India. Through the works of prominent writers such as R. K. Narayan, Salman Rushdie, Arundhati Roy, and Vikram Seth, the article highlights the tension between indigenous culture and the influences of Westernization, colonial history, and modernization. The exploration covers themes such as the impact of colonialism on national consciousness, the search for cultural authenticity, and the negotiation of personal and collective identities in a rapidly changing society. The article also addresses the role of language in shaping Indian identity, acknowledging the challenges and opportunities that arise from using English, a language historically tied to colonialism, to express and redefine what it means to be Indian.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Indian identity,R.K.Narayan,Salman Rushdie,Arundhati Roy,Vikram Seth

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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

Keywords: Transformational Leadership, Job Satisfaction, Job Performance, Meta-analysis

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Creative Commons Attribution 4.0 and The Open Definition