Volume 13 | Issue 4 |

 Abstract

Modern lifestyles expose individuals to various environmental and chemical toxins through processed foods, intoxicating beverages, food adulterants, preservatives, pesticides, and synthetic drugs. Combined with irregular dietary and sleep patterns, these factors contribute to declining immunity and the rise of chronic diseases. If not efficiently eliminated, these toxins accumulate in the body, transforming into Dushivisha-a latent form of toxicity that disrupts physiological balance. Allergic skin diseases are a significant health concern affecting individuals across all age groups, primarily resulting from hypersensitivity of the immune system to specific physical or chemical agents. Classical medical texts, including the Charaka Samhita and Sushruta Samhita, provide detailed insights into the role of Dushivisha in the pathogenesis of allergic skin disorders. Various dermatological conditions attributed to Dushivisha are documented in these texts, including Kustha (a broad category of skin diseases), Mandal (raised circular lesions), Visharpa (erysipelas-like eruptions), Bhinna Varna (skin discoloration), Shonit Dushti (vitiation of blood), Kitibha (psoriasis-like conditions), and Kotha (urticaria or hives).The manifestation and progression of these conditions are influenced by aggravating factors such as exposure to eastern winds, indigestion, cold temperatures, daytime sleep, and the consumption of unwholesome or incompatible foods. These factors contribute to the persistence and exacerbation of Dushivisha, leading to chronic skin hypersensitivity and inflammation. Understanding the role of Dushivisha in allergic skin disorders underscores the importance of detoxification, dietary regulation, and holistic interventions. This review explores the classical perspective of Dushivisha and its relevance to modern dermatological conditions.

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 Keywords

Ayurveda, Dushivisha, Allergic Skin Diseases, Cumulative poison, toxins

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Liposomal drug delivery systems represent a significant advancement in modern pharmacotherapy, providing controlled and targeted delivery of both hydrophilic and hydrophobic drugs. Due to their biocompatibility and ability to encapsulate various therapeutic agents, liposomes have found applications across oncology, infectious diseases, and gene therapy. This review explores the classification of liposomes, advances in drug loading strategies, surface modifications, clinical uses, and future research directions. While several formulations have already gained FDA approval, challenges in manufacturing, stability, and regulatory approval persist. Ongoing innovations, such as stimuli-responsive liposomes and personalized nanomedicine, are poised to further elevate liposomes as a cornerstone in drug delivery technology.

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Liposomes, Drug Delivery, PEGylation, Targeted Therapy, Nano medicine, Controlled Release

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In the Present study an attempt has been made to investigate the Impact of Family environment on Emotional intelligence of Pre-Service secondary school teachers. To achieve the objective of present study a sample of 1113 Pre-service secondary school teachers selected randomly from different B.Ed. Training institutions situated in five districts of Himachal Pradesh. The requisite data is collected from the selected subjects by using the Emotional Intelligence Scale constructed and standardized by the researcher herself and Family Environment Scale developed by Bhatia and Chadha (2012). For analysis of the data, the statistical technique Independent sample t-test was employed. The findings of the study revealed that type of family environment significantly affects the emotional intelligence of Pre-service secondary school teachers. The present study further highlights the suggestions for creating good family environment as well as interventions that promote emotional intelligence among pre-service secondary school teachers.

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Emotional Intelligence, Family Environment, Pre-service secondary school teachers.

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Natural Language Processing (NLP) has emerged as a transformative field within artificial intelligence, enabling machines to understand, interpret, and generate human language. Recent advancements in NLP, driven by deep learning architectures and large language models (LLMs), have significantly enhanced its capabilities across various domains. This paper explores key trends shaping the field in 2025, including real-time language translation, fine-tuning of deep learning models for specialized tasks, semantic search optimization, reinforcement learning applications, and the integration of NLP with other AI domains such as computer vision and robotics. Additionally, the paper highlights practical applications of NLP in healthcare (e.g., medical record analysis and telemedicine), tourism (e.g., automated translation services), and customer service (e.g., chatbots and sentiment analysis). Despite its rapid progress, NLP faces challenges related to bias, explainability, and ethical considerations. Future research directions emphasize transparency, fairness, and scalability to ensure responsible development and deployment of NLP technologies.

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applications,architectures,scalability,analysis,nlp,optimization learning domains,robotics,technologies

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Automated data governance's role in helping cloud based financial data lakes achieve compliance and security is researched. The research addresses the way automated systems handle challenges such as data quality, privacy and regulatory compliance. The scalability of disposal systems is also authorized to help such systems adapt to more and more volumes of data and exchange in regulations. There is less human error involved in data disposal and it continues to be monitored mechanically and approach is inside limits in terms of ensuring data security. The research determines recommended practices for implementing automated disposal frameworks that prioritized maximal data shelter and regulative compliance. Maintaining the unity and credentials of the fiscal data are authorized in the ever-changing regulative environs and these are authorized practices.

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financial data lakes, automated data governance, data quality, compliance, security, scalability, governance frameworks, privacy, data protection, regulatory compliance.

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The study explores the budgeting challenges and financial habits of CA Article students in Mumbai, a city known for its high cost of living. It is important to understand the budgeting habits of students undergoing CA practical training in Mumbai and face financial challenges. CA Articleship is a very vital part of any CA Student's journey and hence it is important to focus on the problems students face in this period. The purpose of the study is to examine the income, expenses & saving pattern of students and to identify challenges and strategies used for financial management. A structured questionnaire was used to collect primary data and interviewed a few students as well after which inferences have been drawn. Data from surveys and interviews reveal that while stipends are a primary income source, they are often insufficient to cover essential expenses like rent and transportation. The findings highlight the need for better financial planning tools and institutional support to alleviate these challenges, ultimately contributing to improved financial well-being among CA students.

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Budgeting, CA Articleship, Cost of Living, Financial Management.

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Customer loss or churn is an enormous issue for most firms currently. Regarding this, the computation of the churn rates will most definitely help the level of customers retained in and also improve the level of profit generation. It explains the most often-used algorithms of machine learning algorithms: logistic regression and random forests for modeling churning from historical data. It elaborates on specific features like demographics and history of transactions, which help to increase results on this model. That too, at a machine learning platform, if proactive retention exists, outputs are even more superior compared to normal practice. Further work might be related to incorporating real-time analytics along with deep learning to create even better predictions for predictability related to churn in the future

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Customer Churn Prediction, Machine learning, Logistic Regression, KNN Algorithm, Random Forest Algorithm, ROC Curve.

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Abstract Cloud computing has revolutionized data storage and sharing by providing scalable and cost-effective solutions. However, ensuring the security of shared data remains a critical challenge due to threats such as unauthorized access, data breaches, and insider attacks. This paper explores various security mechanisms for secure data sharing in cloud environments, including encryption techniques, access control models, identity management, and blockchain-based solutions. Furthermore, it analyzes the role of homomorphic encryption, attribute-based encryption (ABE), and multi-factor authentication (MFA) in enhancing security. The paper also discusses emerging threats and potential future advancements in securing cloud data sharing. The findings highlight the importance of integrating robust cryptographic techniques with advanced access control policies to achieve confidentiality, integrity, and availability in cloud-based data sharing systems.

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Keywords: Cloud computing, Data security, Encryption, Access control, Blockchain, Secure data sharing.

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In addition to being dangerous, skin conditions including melanoma, eczema, and impetigo are frequently communicable, so early and precise diagnosis is essential for successful treatment. However, diagnosis usually calls for specialized understanding of dermatology, and even experts sometimes misclassify disorders, which can result in improper therapy. Our project suggests a deep learning and image processing-based skin disease detection system that uses a Convolutional Neural Network (CNN) model for precise classification in order to overcome this difficulty. This system, which is a personal computer-based program, can be implemented in places with limited resources or in remote locations, improving access to first diagnostic assistance. The system evaluates and categorizes the condition by examining a user-provided photograph of the affected skin area, providing pertinent medical information along with the projected disease.

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AI/ML, Python, Frameworks, etc.

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Malware is an increasing threat to cybersecurity, causing significant harm to systems, data, and networks worldwide. As cyberattacks become more sophisticated, it's crucial to develop reliable and effective methods for detecting and preventing malware. This project addresses this issue by using the Random Forest algorithm, a well-established machine learning technique known for its ability to handle complex datasets and deliver high accuracy. The model is trained to analyse features extracted from executable files, such as file size, code structure, and patterns in behaviour, to distinguish between benign and malicious software. One of the key advantages of the Random Forest approach is its ability to adapt to new and evolving types of malware, ensuring it stays effective as threats change. Additionally, its resistance to overfitting and scalability make it a robust choice for detecting even the most sophisticated malware. Ultimately, this project aims to enhance cybersecurity by providing a flexible, precise, and scalable solution for malware detection.

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Malware Detection, Random Forest Algorithm, Machine Learning, Predictive Accuracy, Malicious Software, Malware Identification.

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