Category Archives: Uncategorized

Authors: Warish Patel, Nana Yaw Duodu, Arpita Sahu, Raksha Choudhary, Sakshi Sharma, Achala Karn

Abstract: The food and beverage sector is undergoing rapid digital transformation, which has in- creased the need for flexible, efficient, and user-friendly ordering platforms. Conventional restaurant systems often struggle with problems like order mismatches, extended waiting times, heavy dependence on staff, and outdated payment methods. To overcome these challenges, we introduce Swift Kiosk, a digital kiosk solution tailored for caf´es and restaurants. Developed using Electron.js, Swift Kiosk en- hances the dining experience by offering customizable ordering, seamless UPI-based payments, and reduced reliance on manual operations. A key highlight of Swift Kiosk is its meal customization feature, where customers can choose portion sizes, ingredients, dietary options (such as vegan, keto, or gluten- free), and preferred cooking styles. The system displays a real-time preview of the customized dish, ensuring precision before order confirmation. Orders are then sent directly to the kitchen system, which minimizes delays and reduces the chance of human error. Additionally, secure UPI integration enables quick, cashless transactions for a smoother checkout process. By streamlining order-taking, payment, and analytics, Swift Kiosk helps restaurants lower labor re- querulents while delivering higher customer satisfaction. This technology-driven approach modernizes traditional restaurant operations, cutting costs, improving efficiency, and creating a customer-focused dining environment. As the industry continues to adopt digital innovations, Swift Kiosk stands out as a scalable, sustainable, and future-ready solution for restaurant management.

Published by: vikaspatanker

Authors: Mohammad Usman M, Bhuvaneswari B

Abstract: The rapid digitization of global infrastructure has led to an exponential increase in cyber threats. Traditional security mechanisms are proving inadequate against evolving, intelligent, and large-scale cyberattacks. Artificial Intelligence (AI) has emerged as a transformative tool in cybersecurity, enabling automated detection, proactive threat prediction, and adaptive defense mechanisms. This paper explores how AI enhances threat intelligence through predictive analytics, machine learning, and deep learning techniques. It also examines challenges related to data privacy, model interpretability, and adversarial attacks. The study concludes that AI-driven threat intelligence has immense potential in transforming cyber defense, provided that human oversight and ethical frameworks are maintained.

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Authors: Dr. Radha Shirbhate, Zaidkhan Pathan, Aditya Gude, Vishal Joshi

Abstract: Mental health plays a vital role in determining overall well-being, productivity, and social interaction [1], [2]. However, diagnosing mental disorders like depression and anx- iety often relies on self-reporting and therapist observation, which may introduce subjectivity and delay treatment. This paper presents an AI-based facial expression recognition (FER) framework that analyzes human emotions from visual cues to assist early mental health assessment [3], [4]. The proposed system uses Convolutional Neural Networks (CNNs) trained on the FER-2013 dataset, combined with MediaPipe for real-time facial landmark extraction and OpenCV for image preprocessing. The model recognizes seven basic emotions: happy, sad, fear, anger, disgust, surprise, and neutral. Real-time video streams are processed, and the detected emotional states are visualized on a dashboard that can track emotion trends over time [5]. The system demonstrates promising performance with accuracy above 92% on validation data and real-time latency under 40 ms/frame [6]. The integration of FER technology into mental health analysis offers an innovative, non-invasive, and continuous monitoring tool that complements traditional clinical methods.

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Authors: Praveen B, Sripadma R

Abstract: Social media platforms such as Facebook, Twitter, Instagram, and WhatsApp have emerged as primary channels for public communication, information sharing, and social interaction. However, the same platforms also serve as spaces where abusive expressions, offensive remarks, and hate speech are increasingly common. Hate speech may target individuals or groups based on factors such as religion, nationality, gender, ethnicity, or other identity characteristics, and can result in psychological harm, discrimination, and real-world conflict. Manual moderation of such continuously increasing online content is challenging, inconsistent, and time-consuming. Therefore, automated detection systems are needed to analyze and classify harmful language. This research proposes a Natural Language Processing based system that preprocesses text, extracts features using TF-IDF, and classifies content using Support Vector Machine (SVM). The results show that this approach effectively distinguishes between normal, abusive, and hate speech, making it suitable for real-time moderation in social media platforms.

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Authors: S. Surendar Raj, K. Sai varsha

Abstract: Chat applications have emerged as indispensable tools on smartphones, offering users the ability to exchange text messages, images, and files at no cost. However, ensuring the security of these communications is paramount. This paper proposes a secure chat application that implements End-to-End encryption, safeguarding private information exchanged between users and providing robust data protection. The application also addresses storage security concerns. By outlining a set of requirements for a secure chat application, this paper informs the design process. The proposed application is evaluated against these requirements and compared with existing popular alternatives to assess its security features. Furthermore, the application under goes rigorous.

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Authors: Krishna Wable, Bhumika Raut, Radhika Ware, Achal Patil, Prof.Vrushali Channe

Abstract: Maintaining a healthy lifestyle often becomes diffi- cult due to generic advice and delayed health insights. Health- Sync: SmartCare Companion is an AI-driven system designed to offer personalized, adaptive, and culturally relevant healthcare support. It integrates BioMistral-7B for medical text understand- ing and ResNet for food and symptom image recognition. Using fuzzy logic with TOPSIS/AHP, the system estimates personalized daily nutrient and calorie needs, supported by an ontology-based food graph linking Indian dishes, nutrients, and diseases. With time-series tracking and reinforcement learning, HealthSync continuously refines recommendations based on user progress. Trained on Indian food and lifestyle datasets, it delivers accurate, real-time, and inclusive wellness guidance—transforming preven- tive healthcare into a smarter, more user-focused experience.

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Authors: Vasudev Sharma

Abstract: The proliferation of enterprise data across multi-cloud infrastructures has intensified the complexity of managing data integrity, regulatory compliance, and interoperability in distributed ecosystems. This study introduces a cognitive data governance pipeline designed to autonomously orchestrate, monitor, and optimize governance functions across heterogeneous enterprise platforms such as SAP HANA, Oracle Autonomous Database, and cloud-native storage systems. Unlike conventional rule-based frameworks, the proposed model leverages deep learning, semantic reasoning, and federated policy orchestration to build adaptive feedback loops that detect inconsistencies, predict compliance deviations, and self-correct data governance pathways in real time. The architecture integrates an explainable intelligence layer that interprets anomaly behaviors, traces root causes, and supports transparent auditability without human intervention. Through simulated deployments in hybrid cloud environments, the framework demonstrates measurable improvements in compliance latency, metadata synchronization, and decision throughput, achieving over 35 percent higher efficiency in governance verification and 25 percent reduction in manual remediation efforts. The cognitive design transforms governance from a static, policy-driven function into a dynamic, self-learning system capable of aligning continuously with regulatory and operational shifts. The findings advance the notion of autonomous governance intelligence as a foundational component of modern multi-cloud data ecosystems, offering enterprises a sustainable pathway toward resilient, compliant, and self-regulating digital operations.

DOI: https://doi.org/10.5281/zenodo.17637615

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Authors: Arpita Sahu, Dr. Waris Patel

Abstract: The rapid digital transformation in the food and beverage industry has created a demand for efficient, customizable, and user-friendly ordering solutions. Tra- ditional restaurant operations often face challenges such as order inaccuracies, long wait times, dependency on labor, and inefficient payment systems. To ad- dress these issues, we propose Swift Kiosk, a digital kiosk application designed specifically for caf´es and restaurants. Built using the MEARN stack (MongoDB, Express.js, Angular, React, and Node.js), Swift Kiosk enhances the food ordering experience by allowing customers to customize their meals, process cashless trans- actions, and reduce dependency on manual labor. This innovation streamlines restaurant operations while delivering a seamless and engaging customer experi- ence. One of the core functionalities of Swift Kiosk is food customization, allowing users to personalize their meals and beverages with ease. Customers can select portion sizes, ingredients, dietary preferences (vegan, gluten-free, keto, etc.), and cooking methods, ensuring a tailor-made dining experience. The application pro- vides a real-time preview of the customized meal, ensuring accuracy before order placement. Swift Kiosk leverages MongoDB as the database to efficiently store and manage user preferences, order details, and restaurant menu configurations. With Node.js and Express.js powering the backend, the system ensures fast and scalable pro- cessing of customer orders, reducing wait times and improving order accuracy. To enhance user interaction, the application is designed using React.js for the frontend, offering a responsive and visually appealing interface. For businesses, an Angular-based admin panel allows restaurant owners to manage menus, track sales, and analyze customer behavior in real time. This combination of technolo- gies ensures a smooth and dynamic user experience. Swift Kiosk automates the order-taking process, reducing the reliance on hu- man staff and increasing efficiency. Customers can place their orders directly through the kiosk interface, which sends requests instantly to the restaurant’s kitchen system. The application supports multiple payment options, including UPI, digital wallets, credit/debit cards, and QR-based transactions, ensuring se- cure and hassle-free payments. By integrating Express.js and MongoDB, transaction data is stored securely, and real-time payment confirmations ensure a frictionless checkout process. This automated system eliminates long queues, minimizes order miscommunication, and enhances customer satisfaction. By digitizing order placement and payment, Swift Kiosk reduces labor depen- dency, allowing restaurant staff to focus more on food preparation and service rather than manual order management. The application also provides data ana- lytics using MongoDB’s aggregation framework, helping restaurant owners analyze customer preferences, order trends, and peak business hours. These insights assist in optimizing menu offerings, pricing strategies, and targeted promotions, enhanc- ing overall profitability. Swift Kiosk, powered by the MEARN stack, presents a modern, scalable, and efficient solution for caf´es and restaurants. By allowing customers to customize their food, automating the ordering and payment process, and reducing opera- tional inefficiencies, Swift Kiosk transforms traditional restaurant operations into a tech-driven, customer-centric model. As the food service industry continues to embrace digital transformation, Swift Kiosk stands as a pioneering step towards enhancing efficiency, reducing operational costs, and improving the overall dining experience.

DOI: http://doi.org/10.5281/zenodo.17637230

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Authors: Anish Mukhiya, Raushan Kumar Ram, Anuraj Chaudhary, Omkar Mahto

Abstract: Background: Personal Desktop Voice Assistants (PDVAs) represent a significant advancement in human-computer interaction, offering intuitive voice-based control of desktop environments. Meth- ods: This study employed Python programming language with integrated speech recognition, natural language processing, and text-to-speech technologies to develop an accessible PDVA. The system archi- tecture incorporates wake word detection, intent recognition, and task execution modules. Results: The developed PDVA successfully executes diverse commands including web searching, media control, system management, and information retrieval. The system demonstrates particular effectiveness for visually impaired users, providing multimodal feedback through synthesized speech and on-screen text. Conclu- sion: The research confirms that Python-based PDVAs can significantly enhance desktop accessibility while highlighting the importance of addressing privacy concerns and interaction limitations in future developments.

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Authors: Isha V Solanki, Preet Darji, Jainil Parmar, Manush Desai, Prof. Garima Sharma

Abstract: The paper addresses an in-depth review of over 60 peer-reviewed studies that deal with canine health and welfare and their proponents and cons on both conventional and emerging strategies. The review encompasses topics of behavioral assessment, vaccination, dental hygiene, population management, diagnosis of allergies and the introduction of artificial intelligence into veterinary medicine, aligning clinical science and technology and ethics. It is worth noting that it determines the vital areas of investigation of behavioral and dental diagnostics, issues of population affected by stray populations, and lack of standardiza- tion in the implementation of preventive care despite its evident usefulness. In contrast to the previous reviews, the given study is rather multi-dimensional, as it links welfare and innovation to each other. The paper can be reproducible because it adheres to PRISMA standards on the selection of studies. Conclusions will target both the veterinarians, the pet owners, researchers, and policymakers by promoting evidence-based models of care. In the perspective, the paper highlights the necessity of standard behaviors and tools, available AI-aided diagnosing, and more significant public education so as to drive international levels of canine well-being.

DOI: https://doi.org/10.5281/zenodo.17636958

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