Volume 13 | Issue 3 |

 Abstract

This study explores the effectiveness of K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN) in predicting patient test results and admission types in healthcare settings. By utilizing a balanced dataset, the research evaluates these models based on key performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC curves. The study aims to analyze the predictive capabilities of these algorithms and their potential to improve healthcare resource allocation and clinical decision-making. Through a comparative analysis, the research highlights the strengths and limitations of KNN and ANN, providing valuable insights into their applicability for real-world medical data. The findings demonstrate how machine learning models can enhance diagnostic accuracy, optimize patient management, and contribute to data-driven healthcare strategies.

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 Keywords

K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Healthcare Predictive Analytics, Machine Learning in Medicine, Patient Test Result Prediction

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 Abstract

Advanced medical diagnostics find their essential role through deep learning, which delivers remarkable accuracy levels in pathology diagnosis and genomic analytics. Researchers investigate how sophisticated algorithms, especially convolutional neural networks, process histopathological slides and genomic information quickly and precisely. Analysis of various datasets through standardized evaluation criteria proves the feasibility of artificial intelligence in early disease detection, leading to individualized care plans and enhanced treatment success. The research design demonstrates why data integrity, appropriate algorithm choices, and healthcare system incorporation depend on integrated multidisciplinary work teams. The study produced vital outcomes demonstrating improved diagnostic processes, decreased errors, and wider limitations of remote healthcare diagnostics. This paper examines ethical points, regulatory elements, and the necessity of transparency in AI systems. Advanced medical science research becomes possible after these findings to develop better sophisticated AI implementations thus modernizing precise medical care while building transformative healthcare methods.

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 Keywords

AI Diagnostics, Deep Learning, Precision Medicine, Genomic Analysis, Medical Imaging, Pathology Automation

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Choosing the correct skincare product is essential in today's environment, but customers frequently struggle to find products that suit their particular skin types and worries. The efficacy of current systems, which typically employ CNN algorithms for skin type prediction, is limited by their inability to handle big datasets. We suggest a method that makes use of the ResNet-101 algorithm for enhanced skin type prediction via image processing in order to get over these restrictions. By providing customers with a data-driven, comparative analysis of skincare products, this strategy improves decision-making in the topical pharmaceutical and cosmeceutical industries. The suggested system not only predicts skin type but also uses a weighted scoring algorithm to assess and contrast product chemicals according to their concentration and applicability to particular skin issues. By examining each ingredient's weight over many products, the system provides a score that takes into account the user's skin type compatibility and probable effectiveness. Customers can use this to enter their skin type and concerns, and they will receive tailored product recommendations along with information about the advantages and disadvantages of each choice. In the end, this system gives customers thorough information that enables them to choose products that are suited to their particular skincare requirements and make educated purchasing decisions.

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 Keywords

Skincare, ResNet-101, Image Processing, Skin Type Prediction, Weighted Scoring Algorithm, Product Recommendations.

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 Abstract

The human resource (HR) policies and practices of small-scale industries are examined in this study, with an emphasis on how these companies manage their workforces to support organizational growth and sustainability. In order to stay competitive in the market, small businesses frequently struggle with a lack of resources, budgetary limitations, and the requirement for efficient HR practices. This study examines important HR procedures such hiring, training, performance reviews, pay, and employee relations and evaluates how they affect worker happiness, output, and overall business performance. The study demonstrates the distinctive HR practices used by small businesses and how they modify their tactics to fit the particular requirements of their operations using qualitative and quantitative data gathered from a variety of small-scale sectors.

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 Keywords

Small scale, Limited Resources, HR Practice, HR Policies, performance management

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This essay examines the profound and multifaceted influence of Mahayana Buddhism on Chinese cultural thought from its introduction during the Eastern Han Dynasty through its full integration in the Tang and beyond. Unlike other foreign religious traditions that entered China, Buddhism underwent a remarkable process of sinicization, becoming thoroughly integrated into Chinese civilization while simultaneously transforming it. The study traces Buddhism's historical development in China and analyzes the formation of distinctively Chinese Buddhist schools including Chan (Zen), Pure Land, Tiantai, and Huayan. It explores the philosophical dialogue between Buddhist concepts like emptiness (??nyat?) and dependent origination and indigenous Chinese thought systems, culminating in the "harmony of the three teachings" paradigm that characterized Chinese intellectual history. The analysis further demonstrates how Buddhist influences permeated Chinese literature, poetry, visual arts, and architecture, creating new forms of cultural expression. Finally, the essay examines Buddhism's impact on Chinese social ethics, institutional structures, and ritual practices. This historical case study illustrates how cross-cultural religious transmission can produce creative synthesis rather than mere replacement or assimilation, offering insights into processes of cultural exchange that remain relevant in today's increasingly interconnected world.

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 Keywords

Mahayana Buddhism, Sinicization, Cultural synthesis, Chinese thought

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The study investigates the socio-legal subtleties of live-in relationships in India, wherein the traditional matrimonial tradition continues to dominate social expectations. The study aims to analyze the evolving judicial interpretations and legal ambiguity related to live-in relationships while observing the social comprehension of the same. A mixed-method research design was adopted, combining qualitative and quantitative techniques. Data was gathered using structured questionnaires, focusing on persons in live-in relationships from different demographic groups in India. Stratified random sampling was employed to ensure representation across age, gender, and place. Statistical methods like regression analysis were employed using MS Excel and SPSS to evaluate the effect of socio-legal issues and judicial interpretations on the legal status of live-in relationships. The study highlights that although judicial precedents have contributed significantly to lending some legitimacy to these relationships, legal ambiguities and social opposition continue to exist. The analysis also detects important demographic factors which determine live-in relationship acceptability in India. Finally, it suggests policy interventions to create an open legal framework balancing constitutional rights and changing social mores, providing protection to those in those relationships.

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 Keywords

Live-in Relationship, Legal Status and Socio-Legal Challenges

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 Abstract

Azadirachta indica is a member of Mahogany family. It is a sacred gift of nature. Azadirachta indica is used in India as an age-old treatment for diverse medical conditions. The Latinized name of neem is "Azadirachta indica," Azad means "free," dirakht means "tree," and i-Hind means "of Indian origin." Thus, it literally means "The free tree of India."1 Azadirachta indica recognized around the world as a broad-spectrum pesticide and fertilizer, neem has applications in agriculture and beyond. Currently, the antimicrobial activities of Azadirachta indica are being explored through research in the fields of dentistry, food safety, bacteriology, mycology, virology, and parasitology.2 This plant is highly abundant in flavonoids, polyphenols, diterpenoids, triterpenoids, limonoids, tannins, coumarin, nimbolide, azadirachtin, azadirone, azadiradione, and gedunin. Some parts of Azadirachta indica, including the leaves in various forms, have been used for wound healing or as a repellent. 3 Neem fruits, leaves, bark, and roots have specific virtues. They have been traditionally used for a huge part of human history and are documented in Sanskrit texts. Due to human activity in trade and travel the plant species has been distributed around the globe and is cultivated in many tropical, and subtropical regions.4

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Azadirachta indica, Anti microbial activity, Phytochemicals, Microorganisms

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's digital landscape, personal or virtual assistants have become essential tools, significantly enhancing the productivity and efficiency of both individuals and businesses. This project focuses on the development of an advanced desktop-based virtual assistant designed to automate a variety of tasks by accepting, comprehending, analyzing, and executing user commands. By streamlining routine activities, the assistant saves time and increases overall user efficiency. The virtual assistant employs cutting-edge machine learning techniques and natural language processing technologies to interpret voice commands accurately. It draws data from multiple online sources, such as search engines, social media, and other information platforms, to continuously enhance its capabilities. This allows the assistant to adapt to a wide range of user requests, providing dynamic and precise responses. For instance, the assistant can retrieve real-time data, such as weather forecasts or news updates, ensuring that users always have access to the latest information. The core technologies that power this system include voice activation, automatic speech recognition, text-to-speech synthesis, natural language understanding. These technologies work together to create a smooth and intuitive user experience, where the assistant can execute desktop tasks, provide information, and respond to queries in real-time. Additionally, advanced web scraping techniques enable the assistant to gather up-to-date information from the internet, enhancing its ability to handle complex requests. As technology evolves, the need for more accessible and efficient ways to interact with computers becomes increasingly important. While traditional methods of input, such as keyboards and mice, are still widely used, voice-based interaction offers a more convenient alternative. This is especially beneficial for individuals with visual impairments who may struggle with traditional input methods. A voice assistant allows them to interact with their computer easily, executing commands and receiving feedback through voice or other accessible means. The primary goal of this project is to create a versatile and inclusive virtual assistant capable of automating tasks, retrieving real-time information, and improving user productivity. By combining voice recognition, task automation, and dynamic information retrieval, the assistant provides a seamless and efficient experience for all users. In doing so, it not only enhances daily productivity but also ensures greater accessibility for those who rely on voice commands to interact with their devices.

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 Keywords

Keywords: - Virtual Assistant, Voice Command, Task Automation, Machine Learning, Natural Language Processing, Speech Recognition, Text-to-Speech, Dynamic Information Retrieval, Web Scraping

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of e-commerce has transformed consumer behaviour, particularly in the online apparel sector. This conceptual study explores the key factors influencing online apparel buying behaviour using the FFF Model (Factors, Facilitators, and Frictions Model). The study synthesizes existing literature and theoretical perspectives to identify the psychological, technological, and economic determinants shaping consumer decisions. Key factors such as personal preferences, brand perception, and social influence play a crucial role in online purchases. Facilitators like website usability, AI-driven recommendations, and seamless payment systems enhance the shopping experience, while frictions such as security concerns, size mismatches, and delayed deliveries act as barriers. Additionally, trust in e-retailers and post-purchase experiences significantly impact customer satisfaction and repeat purchases. The study contributes to the understanding of digital consumer behaviour by providing a structured framework that can aid retailers in optimizing their strategies. Future research can empirically validate the FFF model and explore emerging trends such as augmented reality (AR) in online fashion retail.

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 Keywords

Consumer Behaviour, Digital Shopping Experience, E-Commerce, FFF Model, Online Apparel Buying

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 Abstract

This paper provides a comprehensive literature survey on major advancements in text and speech processing technologies, such as text-to-speech, speech-to-text, brain-computer interface, cross-lingual translation, and summarization. These technologies have made significant progress in recent years, introducing large language models to the industry for seamless interaction by incorporating techniques that generate apt text to the given prompt by improving accuracy and adapting it to different domains. The inputs are accepted in both text and speech formats for text translation and summarization. This allows the system to process and generate translations and summaries directly from typed or spoken content. For speech-to-text, new adversarial algorithms help improve the model's ability to handle challenging or unpredictable conditions, like noise or distortion in speech. While recent TTs models, like Natural Speech, TextrolSppech, and Style TTs 2. These create natural-sounding voices in languages with limited training data by using style control and VAE (variational Autoencoder) models. Cross-lingual summarization and BCIs are also evolving, with systems like DeWave that translate between languages and from ECG to text, simplifying human-computer interaction. Additionally, Sequence-to-sequence models upgrade speech synthesis and recognition by enhancing their ability to express different ideas in various contexts. Overall, digital signal processing, deep learning, and cross-model learning are leading to the emergence of more flexible, user-friendly, and accurate language technologies for communication.

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 Keywords

Text-to-speech, Speech-To-Text, Brain-Computer Interface, Cross-Lingual Translation, Summarization, Large Language Models, Deep Learning, Digital Signal Processing, Sequence-To-Sequence Models, Adversarial Algorithms, And Human-Computer Interaction.

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Creative Commons Attribution 4.0 and The Open Definition