Even if technology has been rapidly adopted in healthcare, a large number of small clinics still use outdated Patient Management Systems that do not offer much cloud integration, remote access, or communication tools. However, they hinder efficient patient data management and compliance in terms of security standards or quality care delivery. In this case, the project proposes a modern patient management system meant specifically for small clinics. It is a complete but light solution that will help in streamlining operations, improving patient care, and bettering the effectiveness of administration. The system uses cutting-edge technologies to buttress the gap and conversion from legacy systems in meeting modern healthcare demands. The patient management system will provide patient registration, schedule appointments, and hold video conferencing such that a clinic could handle interactions with patients while being able to assess timely online consultations. The backend ensures user authentication, file management, and data storage with secure and efficient Appwrite. Next.js, TypeScript, and TailwindCSS make up the frontend which responds in an intuitive interface across devices, improving the experience for healthcare providers and patients alike. Central to the system is secure data and compliance. It has met HIPAA standards, thus ensuring proper handling and storage of sensitive patient data. It also integrates with Sentry for real-time monitoring and error detection, enabling fast resolution of problems and continuous improvements in performance. The two major factors for this solution are cost and scalability. Unlike the over-burdened, complex traditional high-priced systems, this new PMS provides all essential features in a straightforward and affordable package. Hence, no small clinic would be turned away due to financial or operational encumbrance.

Keywords : Liver, Patient, TailwindCSS , Cancer , Machine Learning.

Author : Ravichander Venkatareddygiri , S. Dilli Babu

Title : Patient Management System

Volume/Issue : 2025;2(2)

Page No : 1 - 7

Encryption makes it possible to send private information via unprotected channels without worrying about data loss or modification by unauthorized parties. For data security in various contexts, many encryption algorithms have developed throughout time. This paper focuses on symmetric encryption, often used for key exchange due to its large key size. In the context of data centers and cloud computing, security is paramount. This paper proposes a quantum computing algorithm for encrypting cloud data. This combination leverages quantum computing's rapid complex computations. The quantum computing algorithm offers fast, efficient, and secure data protection in cloud environments, reducing computational power requirements while enhancing overall efficiency. Moreover, it addresses the limitations of traditional encryption methods and provides a scalable solution for large-scale data encryption. By integrating quantum computing, we can achieve unprecedented levels of security and performance. The potential impact of this approach could revolutionize data protection standards in the cloud industry.

Keywords : Encryption, Sensitive Data, Insecure Channels, Data Loss, Large-Scale Data Encryption, Cloud Industry.

Author : V Sujana , N Bindu Madhavi

Title : Advancing Cloud Data Protection with Secure Cryptographic Algorithms

Volume/Issue : 2025;2(2)

Page No : 8-14

The inability to communicate effectively because of communication barriers severely restricts deaf and mute individuals from getting in touch with those who do not understand sign language. A new web-based system has been developed to provide an effortless communication solution between users who are deaf, mute and sign language users. The system implements Django as its backend framework together with text-to-sign language conversion and speech-totext capabilities to establish successful communication. Through the application users have the option to provide text or audio content that gets translated into American Sign Language (ASL) animations through hand gesture visualization from MediaPipe. The system enables automatic speech recognition through the SpeechRecognition library and functions as an ASR tool to convert spoken words into text. The Google Translator API enables native language translation of this recorded text which expands communication possibilities to different user groups. From datasets obtained through Kaggle the training of a Convolutional Neural Network (CNN) model reaches 99% accuracy for sign recognition leading to accurate communication. The application creates a user-friendly interface which enables people with speech and hearing difficulties to get real-time sign language animations as visual outputs. The novel system surpasses typical assistive communication solutions because it cuts out the requirement for human sign language interpreters to provide inclusive communication channels. This proposed web application will increase accessible interactions thus promoting more social integration opportunities for both deaf and mute users. The system will be improved through language expansion of sign language interpretation and a mobile app adaptation for maximized accessibility.The system incorporates components related to Communication accessibility, sign language recognition, speech-to-text, ASL animation and employs CNN models in conjunction with Django framework,

Keywords : Media Pipe, Automatic Speech Recognition, Google Translator API , Web-Based Assistive.

Author : Konda Hari Krishna , B. Suchitha , B. Pravallika , E. Deepika , G. Jaahnavi

Title : Empowering Communication: A Web Application for Deaf, Mute, and Sign Language Interpretation

Volume/Issue : 2025;2(2)

Page No : 15 - 21