Volume 13 | Issue 4 |

 Abstract

Attitudes towards different hair, such as hairstyles and hair removal, vary widely across different cultures and historical periods, but it is often used to indicate a person's personal beliefs or social position, such as their age, sex, or religion. As hair is considered as barometer of beauty each one is over conscious about it and is in search for a better remedy for the maintenance and management of their crowning glory. Alopecia areata is characterized by acquired skin aliment that can affect all the hair bearing skin including scalp, beard, which is characterized by localized areas of non-scarring hair loss which can be compared with Indralupta. In Ayurveda literature Acharyas had briefly explain hair and mentioned many more modalities to treat hair loss. Nidana Parivarjana, Prachanna, Shamanoushadhi and externally use of Lepa. Prachanna Karma is a specialized form of Raktamokshana (bloodletting) in Ayurveda, primarily utilized for expelling vitiated blood and treating localized disorders. Indralupta, which according to modern goes with the name as alopecia, a condition characterized by sudden hair loss, is often associated with vitiation of Pitta and Kapha Doshas, leading to obstruction of hair follicles. This research explores the effectiveness of Prachanna Karma in the management of Indralupta, its historical references, procedural methodology, and modern clinical relevance. A thorough review of Ayurvedic texts, along with contemporary findings, is presented to establish the efficacy of this treatment.

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 Keywords

Alopecia areata, Indralupta, Ayurveda, Prachaanakarma, Lepa.

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 Abstract

As we know, many people suffer from unhappiness in today's world, so there is a requirement for a system that recommends music based on human emotions such as anger, sadness, happiness, etc. In this paper, we propose a system named Multimodal Emotion Detection which helps people by suggesting music and movies based on their emotions. We implemented this system using the FER- 2013 dataset, which contains 35,887 images of different emotions such as sadness, happiness, neutral , surprise, disgusted, fearful, and anger. We used Python's TensorFlow framework and Haar Cascade algorithms to recommend movies and music based on human facial emotions. The main concern in existing recommendation systems is manual sorting. To avoid this, we propose this model, which automatically plays music and movies without requiring much browsing time.

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 Keywords

Image processing, Facial recognition, Movie Recommendation, Song Recommendation . 2 [1-2]

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Emotional stability plays a vital role in personal well-being and everyday functioning. This research explored the use of data analytics to evaluate and comprehend emotional stability, concentrating on psychological, behavioral, and environmental elements like stress. By examining survey responses, communication trends, and psychological evaluations, the study compared the reliability of the Cronbach and Omega methods. Gated Recurrent Unit (GRU) models were employed to forecast stress levels among student groups. The research developed predictive models for outcomes such as overall emotional stability, the risk of mental health problems, academic satisfaction, perceived social support, and stress levels. The analysis aimed to identify significant connections between input features and predicted outcomes, assess model performance using relevant metrics, and determine the importance of individual attributes in predicting students' well-being. It also offers valuable insights into the quantitative measurement of emotional stability and its variations in different situations.

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 Keywords

Cronbach's Alpha, Omega method, Psychometric Analysis, RNN, LSTM,GRU.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Databases store and manage critical data, making them prime targets for security threats. various security issues [1], such as SQL injection, unauthorized access, insider threats, and data breaches, pose significant risks to database integrity and confidentiality. Additionally, improper encryption, weak authentication mechanisms, and inadequate access controls can further expose databases to cyberattacks. To mitigate [3] these threats, organizations implement security measures such as role-based access control (RBAC), multi-factor authentication (MFA), and encryption techniques for data at rest and in transit. Firewalls, intrusion detection systems (IDS), and regular security audits help identify and prevent unauthorized access. Moreover, database activity monitoring (DAM) and the use of secure coding practices minimize vulnerabilities like SQL injection. This seminar discusses various security challenges in databases and explores modern solutions, including blockchain-based security [2], zero-trust architecture, and AI-driven anomaly detection. By implementing robust security strategies, organizations can safeguard their databases against evolving cyber threats and ensure data privacy, integrity, and availability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Database security, SQL injection, unauthorized access, insider threats, data breaches, encryption, weak authentication, access controls, RBAC, MFA, IDS, firewalls, security audits.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Neck pain and disability are prevalent in HNC patients, which can be attributed to the diagnosis itself or its treatment. Neck pain and associated disability may cause psychological distress and subsequently decrease the patients' QoL. This longitudinal prospective study has been undertaken to assess neck pain and related functional and psychological concerns and determine the QOL among HNC patients receiving anti-cancer treatment. 35 head and neck cancer patients undergoing cancer treatment will be enrolled in the study. They will undergo a baseline evaluation before the initiation of treatment and will be followed up at 1st month, 2nd month, and 3rd month.

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 Keywords

Anxiety, depression, head and neck cancer, neck disability, neck pain, QOL

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 Abstract

Heart disease remains one of the leading causes of mortality worldwide, necessitating early detection and accurate prediction to improve patient outcomes. Traditional diagnostic methods often require extensive medical expertise and expensive tests, leading to delays in diagnosis. Machine learning (ML) techniques offer a promising solution by analyzing vast datasets to identify patterns indicative of heart disease. This study explores the application of various ML algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines, to predict heart disease based on clinical parameters such as blood pressure, cholesterol levels, and lifestyle factors. The dataset used for this research is sourced from reputable medical repositories, ensuring reliability and robustness. The proposed model achieves high accuracy in disease classification, showcasing the effectiveness of ML-driven predictions. The findings highlight the potential of machine learning in aiding healthcare professionals with early detection, improving treatment plans, and reducing death rates. (CVDs) account for approximately 17.9 million deaths each year, making them the leading cause of mortality worldwide. Early detection and prevention are crucial in reducing the fatality rate and improving patient survival. Conventional diagnostic approaches, such as electrocardiograms (ECG), echocardiograms, and angiograms, require specialized equipment and expertise, making them less accessible in many regions. In recent years, machine learning has emerged as a powerful tool in the healthcare domain, capable of analyzing complex medical data to identify patterns and predict diseases with high accuracy. By leveraging ML algorithms, healthcare professionals can improve diagnostic precision, automate risk assessment, and provide personalized treatment recommendations. This study focuses on applying various machine learning techniques to predict heart disease using clinical datasets. The primary objective is to develop a model that can accurately classify individuals at risk of heart disease based on key health indicators, including age, cholesterol levels, blood pressure, diabetes status, and lifestyle habits.

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 Keywords

Angiograms, Blood Pressure, Decision Tree, Electronic Health Records, ECG, FBS, Heart Disease Prediction, Logistic Regression, Machine Learning Models, Random Forest, Support Vector Machines.

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 Abstract

The present study was aimed at diabetic studies with orally administered aqueous leaf extract of Ocimum sanctum Linn (OSE). In this study four groups of mice (n=6/group) were tested, in which the first group the normal group, the second group diabetic control group was injected with 70 mg/kg bw alloxan, and the third and fourth groups of diabetic mice were orally treated with doses of 100 and 200 mg/kg bw of O. sanctum for 28 days, and a blood glucose study was taken. Treatment with OSE did show some changes in their blood glucose.

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 Keywords

O. sanctum, Antidiabetic, Alloxan, Glucose , Aqueous, Antioxidant

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A straightforward framework for creating wireless network protocols incorporates localized broadcasting and routing. The basic foundation is predicated on the cost-benefit ratio of specific choices, such cutting back on the number of distances. In this wireless network application, one of the main networks is the routing protocol. These are some of the most often used routing strategies in network layers. In the distributed routing in every node determines if routing through a certain neighbor is less expensive than using nodes that are already in use.

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Packet Delivery, Routing Protocols, Network Layer, IPv4, IPv6, ICMP, RIP, and Network Efficiency

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Artificial intelligence (AI) within financial technology (FinTech) platforms creates radical changes in customer-related activities and operational performance while adjusting service delivery practices. This research examines AI-based service solutions that affect user interactions through evaluations of service quality as well as trust and personalization, and sustainability. A quantitative research method involved gathering survey information from people who actively use FinTech platforms that incorporate artificial intelligence technologies. Through AI technology, financial service providers enhance customer satisfaction by simplifying delivery systems and providing better ease-of-use options. The level of trust users have in AI encounters problems because of their worries about data protection, together with their need to understand how algorithms work. The research demonstrates why FinTech companies must use AI properly through visible system operations along with security protocols and adherence to rules. The research links technical innovation with user expectations to supply FinTech companies with tactics that optimize Artificial Intelligence applications for better interactions with customers. This study advances the financial service AI knowledge base by presenting applicable solutions for sustainable ethical development of FinTech innovation.

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 Keywords

The analysis incorporates Artificial Intelligence, FinTech, customer Experience and Service Quality, User Perception, Convenience, and Sustainability as important terms.

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Creative Commons Attribution 4.0 and The Open Definition

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This paper rethinks translation studies through an ecological framework, proposing an integrated approach that considers language, culture, and environment as mutually constitutive systems. It moves beyond the traditional source-target text dichotomy to examine how translations function within broader ecological networks of meaning, power, and survival. While translation studies have made significant strides in various interdisciplinary directions, a notable vacuum persists in addressing environmental literature, particularly at the theoretical level. This gap is critical given the rising global concern for sustainability and ecological consciousness. The paper undertakes a critical survey of how environmental thinking has evolved within translation discourse and identifies emerging paradigms that align translation practice with ecological ethics. By highlighting the necessity of an ecologically informed translation theory, and calls for a reorientation of translation studies toward sustainability, responsibility, and interconnectedness.

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 Keywords

Eco-translation, Ecolinguistics, Translation Ethics, Sustainability, Environmental Humanities

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Creative Commons Attribution 4.0 and The Open Definition