Volume 14 | Issue 3 |

 Abstract

Vitamin deficiencies represent one of the most common health problems in many countries of the world, especially where access to medical test facilities is poor. These deficiencies could take the form of impaired immunity, a sight problem, skin diseases, anemia, or poor physical development. Conventional techniques for its diagnosis primarily rely on blood tests, which are very accurate but sometimes invasive, expensive, and ill-suited for mass screening or screening in remote areas. This demands the development of other methods of diagnosis that are non-invasive in nature and yet would facilitate early detection. This project is aimed at the development of an AI-driven system for the detection of vitamin deficiencies using images with visible symptoms from eyes, nails, skin, tongue, and hair by deep learning. In this approach, transfer learning applies the ResNet152V2 model for feature extraction, which uses the attention mechanism to help the model focus on most of the significant parts of the image that are associated with deficiency-related visual patterns, improving the detection of very small manifestations of symptoms. A deep learning approach using a custom dataset of images of different body areas and corresponding labels is used to train and test the proposed system. The proposed model will be able to detect multiple vitamin deficiencies and give accurate predictions. The performance of the proposed model tested using common evaluation metrics such as accuracy(91.4%), precision(91%), recall(90.7%), and F1-score(90.8%), which will show the efficiency of the proposed classification model. The proposed model is expected to be used as a supporting diagnostic tool and will work to decrease the dependence on laboratory work. The proposed model will also be non-invasive and will be easily deployable on a large scale.

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 Keywords

Vitamin Deficiency Detection, Deep Learning, ResNet152V2, Transfer Learning, Image Classification, Visible Symptom Analysis, Remote Healthcare.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Banking institutions increasingly rely on digital document submissions for services such as account creation, loan approval, and customer verification. Rapid digitalization has resulted in increased image-based fraud using forged or manipulated banking documents. This paper proposes an AI-powered image authenticity verification system designed to detect fake document images using deep learning techniques. The system analyzes document images such as Aadhaar cards, PAN cards, and bank statements. Input images undergo preprocessing steps including resizing, normalization, and noise removal. A Convolutional Neural Network (CNN) is used to extract visual features such as texture inconsistencies, edge distortions, and compression artifacts. The system classifies images into genuine or fake categories with high accuracy. Public datasets such as CASIA and Deepfake datasets are used for training and evaluation. The system improves banking security by reducing fraud and automating verification processes .Banking institutions increasingly rely on digital document submissions for services such as account creation, loan approval, and customer verification. Rapid digitalization has resulted in increased image-based fraud using forged or manipulated banking documents. This paper proposes an AI-powered image authenticity verification system designed to detect fake document images using deep learning techniques. The system analyzes document images such as Aadhaar cards, PAN cards, and bank statements. Input images undergo preprocessing steps including resizing, normalization, and noise removal. A Convolutional Neural Network (CNN) is used to extract visual features such as texture inconsistencies, edge distortions, and compression artifacts. The system classifies images into genuine or fake categories with high accuracy. Public datasets such as CASIA and Deepfake datasets are used for training and evaluation. The system improves banking security by reducing fraud and automating verification processes.

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 Keywords

Fake Image Detection, CNN, Banking Security, Image Forensics, Deep Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

"Reversing Lens: Contemporary Gender Biases against Men in Society and Social Media," explores the evolving dynamics of gender inequality by shifting focus toward the often overlooked challenges faced by men. While acknowledging the historical and on-going struggles of women, the study argues that contemporary social, legal, and digital environments have generated new forms of bias that disproportionately affect men.

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 Keywords

Gender bias, Male marginalization, Legal discrimination, False accusations, Social media trials, Masculinity stereotypes, Mental health stigma, Patriarchy, Gender norms, Inclusive gender equality

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: This study examines that I am relationship between green Bond issuance and key and environmental and macroeconomic indicators including renewable energy consumption GDP per capita and CO2 emissions using a multi country data sets for the post 2015 period. The analysis employees a combination of descriptive statistical techniques ,Pearson correlation analysis and multiple linear regression models to access the magnitude and significance of the relationships among these variables. Empirical findings indicate that green bond issuance exhibits statistically significance positive effect on renewable energy consumption. However the impact on co2 emission is found to be moderate suggesting the influence of additional macroeconomic and structural factors. The findings of the study highlight the growing importance of green bonds as a financial instrument for financing environmentally sustainable projects and emphasize the need for strengthening policy support to enhance their effectiveness in achieving long term environmental and economic objectives.

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 Keywords

Green bonds issuance, Renewable energy, Sustainable development, Carbon emissions

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 Abstract

In this paper, a stochastic fractional solid transportation model is developed by considering demand as a random variable following multiple probability distributions to capture uncertainty in logistics systems. The model integrates a fractional objective function within a three-dimensional framework involving sources, destinations, and conveyance modes. Using distributional properties, the stochastic model is transformed into a deterministic equivalent form. The optimal solution is obtained using Karush-Kuhn-Tucker (KKT) optimality conditions. A numerical example is provided to demonstrate the effectiveness and applicability of the proposed approach in transportation decision-making under uncertainty.

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 Keywords

Keywords: Stochastic fractional solid transportation; KKT multipliers; Demand uncertainty; Probability distribution modeling; Optimization under uncertainty.

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Psychological well-being is an essential component of overall mental health and plays a significant role in individuals' ability to cope with daily challenges and stress. Women in their thirties often experience multiple responsibilities related to career, family, and personal development, which may increase psychological stress and affect emotional well-being. The present study aimed to examine the effectiveness of a journaling intervention in enhancing psychological well-being and reducing perceived stress among women aged 30-39 years. The study employed a quantitative pre-test-post-test research design with a sample of 30 participants selected through purposive sampling. Standardized psychological measures were used to assess participants' levels of psychological well-being and perceived stress before and after the intervention. Participants engaged in a structured journaling practice for a specified period, during which they recorded personal reflections and positive experiences. Statistical analysis using descriptive statistics and paired sample t-tests revealed a significant increase in psychological well-being scores and a reduction in perceived stress levels following the intervention. The findings suggest that journaling can serve as an effective and accessible psychological strategy for promoting emotional well-being and managing stress. The study highlights the potential role of journaling-based interventions in mental health promotion among adult women.

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 Keywords

journaling, psychological well-being, perceived stress, women's mental health, positive psychology

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Creative Commons Attribution 4.0 and The Open Definition

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This paper explores the philosophical ideas, practical similarities and differences between Mahatma Gandhi and Sonam Wangchuk as transformative figures in Indian society. While Gandhi's methods are rooted in non-violent resistance and ethical politics, Wangchuk employs innovation and sustainable technology to address contemporary challenges. Through a comparative analysis of their ideas on education, sustainability, modernity, and social reform, this paper argues that Wangchuk represents a modern adaptation of Gandhian principles in a technologically driven world. The study highlights how both figures emphasize self-reliance, local empowerment, and ethical responsibility, though their methods differ significantly due to historical and contextual changes.

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 Keywords

Satyagraha, Ahimsa (Non-violence), Sustainable Innovation, Social Change, Self-reliance, Grassroots Development.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Antibiotics are often called "wonder drugs" because they have been used for decades to fight infections caused by microbes. These drugs are not only used for treating illnesses but are also widely used in agriculture and animal farming to prevent disease. However, a serious problem has emerged: microbes are becoming resistant to common antibiotics, and many people aren't aware that this resistance is developing. The purpose of this review is to look into where antibiotic resistance comes from, how it has developed, and the current challenges in fighting it. Our findings show that antibiotic resistance is growing rapidly. Many infections, like pneumonia, tuberculosis, and gonorrhoea, are becoming harder to treat, and in some cases, antibiotics no longer work at all. This is because the more antibiotics are used, the more likely it is that resistance will develop. The overuse and misuse of antibiotics, especially outside of medical settings (such as in farming), is a major factor in making microbes resistant. Currently, there are very few treatment options for hard-to-treat bacterial infections caused by multidrug-resistant bacteria, leading to higher rates of illness and death. This review emphasizes the need to use antibiotics more carefully in both humans and animals to slow down antibiotic resistance. It also highlights that the public's understanding of antibiotic resistance is still very limited. For this reason, educating both patients and the general public is crucial in the fight against antibiotic resistance.

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 Keywords

Antibiotic Resistance , Antimicrobial Resistance Multi drug Resistance bacteria, Antibiotic misuse and overuse, Superbugs, One health Approach.

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 Abstract

Career selection is a complex and critical decision that significantly influences an individual's professional growth and long-term satisfaction; however, many students and job seekers face difficulties due to limited guidance, lack of awareness about available career options, and uncertainty regarding their own abilities and interests. Conventional career counselling systems often rely on generic recommendations and fail to address individual differences. To overcome these limitations, this research proposes a Personalized Career Advisor based on predictive classification and an intelligent advisory engine. The system collects user-specific attributes such as educational background, academic performance, skills, interests, and career preferences, and applies machine learning-based classification algorithms to analyze patterns from historical student and career datasets. Multiple classifiers are utilized to improve prediction accuracy and robustness in identifying suitable career domains. After predicting the most appropriate career path, the advisory engine generates personalized recommendations, including essential skills, relevant courses, certifications, tools, and a structured career roadmap. Furthermore, the system performs resume analysis to extract key information such as skills, qualifications, and experience, which is then matched with job requirements to suggest relevant and reliable job opportunities. When gaps are identified between user profiles and job eligibility criteria, the system provides targeted recommendations for skill development and learning. Overall, the proposed approach integrates career prediction, personalized guidance, and employment recommendations to deliver an effective and intelligent solution for informed career planning and professional development.

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 Keywords

Career Recommendation System, Machine Learning, Predictive Classification, Advisory Engine, Artificial Intelligence, Resume Analysis, Job Recommendation, Career Guidance System, Skill Gap Analysis, Data Mining, Student Career Prediction, Personalized Recommendation System, Career Path Prediction, Employment Recommendation, Educational Data Analysis.

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 Abstract

The formulated methi-shikkakai herbal shampoo proved to be an effective, natural, and safe alternative to commercial chemical shampoos. The combination of methi, shikkakai, amla, reetha, hibiscus, and other herbal ingredients provided excellent cleansing, conditioning, and nourishing properties. Methi enhanced hair strength and reduced dandruff, while shikkakai acted as a gentle natural cleanser. Reetha contributed rich foaming ability, and amla supported scalp health and shine. The evaluation parameters such as pH, foam height, viscosity, stability, and cleansing ability indicated that the herbal shampoo met acceptable quality standards. Overall, the formulation is cost-effective, environmentally friendly, non-toxic, and suitable for regular use, promoting healthy hair and scalp without side effects. The results indicate that this combination provides excellent cleansing, conditioning, and anti-dandruff properties, making it a safe alternative to synthetic surfactants. Methi (fenugreek) acts as a natural conditioner, making hair soft, manageable, and shiny, which reduces the need for synthetic conditioners.

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 Keywords

Nourishment, Evaluation, Dispersion, Anti-dandruff, Antioxidant.

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Creative Commons Attribution 4.0 and The Open Definition