Volume 13 | Issue 5 |

 Abstract

This study explores customer perception of green finance services offered by Shriram Finance Limited, focusing on the awareness, acceptance, and satisfaction levels among its clientele. As environmental concerns grow and sustainable financing gains traction, financial institutions like Shriram Finance are increasingly integrating eco-friendly loan products and services into their portfolios. The research aims to assess how customers perceive these green finance initiatives in terms of trust, transparency, accessibility, and environmental impact. It also investigates the factors influencing customer adoption, such as awareness levels, perceived benefits, and alignment with personal values. The findings provide insights into how Shriram Finance tailor can better its green financial services to enhance customer engagement and contribute to sustainable development goals.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Green finance, Customer perception, Sustainable banking, Shriram Finance Limited, Eco-friendly loans, Environmental impact, Green lending, Financial services, Sustainability, Customer satisfaction.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents an automated face verification framework that integrates both 3D facial surface analysis and grey-level texture information to enhance recognition accuracy. The 3D facial data is acquired using a structured light-based scanning system, which is specifically optimized for facial geometry capture while ensuring spatial alignment with simultaneously captured grey-scale images. The verification process involves two key similarity measures: (1) geometric matching of the 3D facial surface using residual error analysis, and (2) grey-level texture comparison using normalized cross-correlation techniques. A fusion strategy is employed at the score level to combine both geometric and texture-based similarities, thereby improving overall verification performance. The proposed system is designed with real-time integration in mind, suitable for security and biometric applications where user cooperation is expected. The modular architecture also allows for future integration with deep learning-based feature extraction and matching methods.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Face verification, 3D face recognition, Structured light acquisition, Surface matching, Grey-level texture fusion.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cyperus rotundus, commonly known as nut grass, is a perennial plant widelyrecognized in traditional medicine. This review paper explores the phytochemical composition, therapeutic applications, and potential benefits of Cyperus rotundus, particularly in skincare, highlighting its use in herbal face packs.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cyperus rotundus Botanical properties, Traditional medicine, Phytochemistry Ethnobotany, Antimicrobial activity, Antioxidant properties, Medicinal uses, Invasive species, Agricultura limpacts, Ecological significance, Therapeutic application

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The healthcare sector is one of the largest and most crucial fields, as it addresses people's health conditions and aims to enhance life expectancy. Numerous doctors and medical organizations are actively involved in this field, and we are also contributing to its ongoing development. Many doctors are working day and night in this COVID pandemic to save the lives of these persons who are not even taking care of themselves and we have always learned that "preventions are better that cure", all this pandemic is about taking proper sanitization and hygiene which is the only way to stop this spread to spread and get rid of this problem. But no-body is taking proper measures in the right direction so as a part of this nature we have to contribute to doctors by taking proper measures and reducing the pressure over them. The primary objective behind developing this machine is to support the healthcare sector and assist doctors in maximizing their efforts during the pandemic, as they continue to serve the nation with dedication. The concept involves creating a sanitization device designed to disinfect individuals in an economical and efficient manner, with a strong emphasis on the importance of health. The device will be constructed using 100% recycled and recyclable plastic, promoting sustainability and helping to reduce pollution. Arduino programming will be implemented to measure body temperature and activate the pumps automatically. The use of recycled materials not only lowers production costs but also makes the device an environmentally friendly and cost-effective solution.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Automatic Sanitizer Machine, Sanitizer Machine, Disinfection, Hygiene Machine, Infection Control Machine

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract --- This paper introduces a deep learning-based system for the detection of potholes on road surfaces using image classification techniques. The proposed approach employs a Convolutional Neural Network (CNN) to identify and classify images containing potholes from those without. A custom-designed graphical user interface (GUI) allows users to upload road images for instant analysis and detection. The system is trained on a publicly available dataset of road images, covering various pothole sizes and surface conditions. Designed for offline deployment, the system provides an efficient and accessible solution for road maintenance departments and municipal bodies, enabling timely and accurate road condition assessments.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Index Terms-- Pothole Detection, Deep Learning, Convolutional Neural Network, Road Safety, Image Classification

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents the development of a voice controlled virtual assistant using python

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Python libraries pyttsx3, pywhatkit, speech recognition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Existence of human society and criminal activities go hand in hand but rape is a crime, which is different from all other types of crimes because it robs off dignity of woman and leaves her to suffer throughout her life. There are still many families in India, which consider that life of a woman is over if she is a rape victim. India is a country in which on one hand women are worshipped in the form of deities and on the other hand, on and average 86 girls are raped daily, no matter whether the case is reported or not. Rape is a heinous crime that is against basic human rights and violates the dignity and autonomy of a woman. We are stepping ahead in all directions but cannot go ahead with this stigma on our forehead. The total percentage of reported rape cases is 7.07 (NCRB 2022). This brutal crime is very common in India. The society defends the perpetrators and stigmatizes the victim and her family. The present paper discusses in detail the analysis of various laws related to rape and analyses the reported cases since independence. It also discusses various amendments in rape laws under Indian Penal Code and also suggests recommendations to exterminate this crime from the country.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rape, Woman, Victim, Perpetrator, Crime

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The research aims to explore the concept of workforce planning and strategies for addressing challenges encountered by workforce departments. Understanding the functioning of the workforce planning department and its ability to overcome challenges is essential for enhancing organizational productivity and enabling proactive anticipation of future challenges. The primary objective is to investigate methods for effectively managing the challenges faced by the workforce planning department. Specific objectives include: (1) Identifying challenges affecting the workforce (2) Developing solutions to address these challenges (3) Assessing the specific hurdles faced by the workforce department, and (4) Enhancing workforce efficiency. The study employs a sample study approach, focusing on private companies in Oman. A questionnaire was distributed to 229 employees, and the data obtained underwent thorough analysis. Based on the insights provided by employees, suggestions and recommendations were formulated.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Workforce Planning, Challenges, Strategies.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Brain tumors affect over 300,000 individuals globally each year, posing serious health risks due to their aggressive nature and complex diagnosis. Accurate and timely detection of brain tumors is vital for effective treatment and improved survival outcomes. Traditional manual analysis of Magnetic Resonance Imaging (MRI) scans, although clinically effective, is labor-intensive, prone to inter-observer variability, and relies heavily on specialist expertise--creating diagnostic delays, particularly in resource-constrained settings. This study introduces a comprehensive AI-based framework for automated brain tumor detection and classification, designed to enhance diagnostic accuracy, speed, and accessibility. The proposed system integrates Convolutional Neural Networks (CNNs) for deep feature extraction and Support Vector Machines (SVMs) for efficient classification. It processes MRI scans through a multi-stage pipeline involving grayscale conversion, image resizing, contrast enhancement, morphological segmentation, CNN-based feature learning, and SVM classification to categorize images into Glioma, Meningioma, Pituitary, or Normal. Trained and tested on a Kaggle-sourced dataset comprising 3,050 MRI images, the system achieves an overall accuracy of 91%, with class-specific accuracies reaching up to 95%. It also demonstrates a 60% reduction in diagnostic time when compared to traditional manual analysis. Evaluation metrics such as precision (0.87-0.94), recall (0.88-0.95), F1-score, and an AUC of 0.93 substantiate the model's robustness and clinical reliability. The model's architecture emphasizes modularity and interpretability, offering visual outputs such as segmented tumor overlays and confidence scores, thus aiding radiologist verification and fostering clinical trust. Implemented in MATLAB R2022a, the system supports fast processing on standard CPUs, ensuring adaptability in both high-end and low-resource environments. Beyond technical performance, the study explores ethical and practical considerations such as model explainability, transparency, and equitable deployment. Future enhancements include integrating Explainable AI (XAI) tools like SHAP and Grad-CAM, expanding datasets to cover diverse demographics and rare tumor types, enabling 3D MRI analysis for better spatial resolution, and deploying the system on scalable cloud platforms for global accessibility. By uniting advanced AI techniques with clinical usability, this research contributes a reliable, scalable, and cost-effective solution for neurological diagnostics, with the potential to democratize access to high-quality healthcare, especially in underserved regions. The proposed system not only assists in early tumor detection but also lays the groundwork for future AI-driven innovations in radiological assessment and medical decision support.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Brain Tumour, Tumor, PRECISION TUMOR ANALYSIS, BRAIN TUMOR DETECTION

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Eye Textor is a real-time text reader app that captures and reads printed or handwritten text using a mobile camera. It uses OCR technology to convert text to speech, making it helpful for visually impaired users and those needing quick access to text. The app focuses on speed, accuracy, and ease of use in different conditions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine learning , java

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Creative Commons Attribution 4.0 and The Open Definition