Category Archives: Uncategorized

Authors: Kartavy Gupta

Abstract: Artificial Intelligence (AI) is rapidly transforming the Indian education system, offering personalized learning, improved accessibility, and efficient administration. This paper examines the role of AI in Indian education, combining secondary research with a small-scale student survey. The survey, conducted among 50 senior secondary students from urban and semi-urban schools in India, explored awareness, usage, and perceptions of AI in learning. Results indicate that while 82% of students use AI-powered tools for study support, only 38% receive any formal school-based AI education. The findings highlight both enthusiasm for AI and the need for structured training, equitable access, and ethical guidelines. The study concludes that AI can revolutionize Indian education if implemented inclusively and responsibly.

Published by: vikaspatanker

Authors: Lingaraju, Sathish kumar B N

Abstract: Utility-scale solar park siting in semi-arid regions requires balancing physics-driven energy yield with engineering, environmental, and operations constraints under substantial uncertainty. This paper presents a transparent fuzzy multi-criteria framework that (i) maps raw criteria to physically motivated desirability memberships-monotone linear functions for benefit/cost attributes (global horizontal irradiance, slope, grid distance, water distance, dust days, protected-area buffer, land-use suitability) and a bell-shaped (Gaussian-like) function for module-temperature effects-(ii) derives criterion weights using fuzzy AHP from linguistic pairwise judgements, and (iii) aggregates via a TOPSIS-like closeness measure in the desirability space. A realistic 12- site semi-arid screening dataset is used to demonstrate the workflow. The resulting normalized weights emphasize energy yield (GHI, 0.377) and host-land compatibility (land-use, 0.132; temperature, 0.115), while accounting for terrain (slope, 0.092), network access (grid distance, 0.081), O&M logistics (water distance, 0.052; dust days, 0.058 ), and biodiversity safeguards (protected buffer, 0.092). The final closeness coefficients rank sites S12," " S10, and S 02 as top candidates, driven by high irradiance ( ≥6.2kWhm^(-2) day ^(-1) ), gentle slopes ( <3% ), reasonable grid proximity, suitable land cover, and adequate environmental buffers. Sensitivity checks indicate ranking stability under modest weight perturbations, reflecting strong physical signal in the data. The framework is minimal, auditable, and readily swappable with GIS-derived layers, making it suitable for early-stage planning, stakeholder dialogue, and policy screening in semiarid contexts.

DOI: http://doi.org/10.5281/zenodo.17646055

Published by: vikaspatanker

Authors: Md. Abdul Momin, M.M. Musharaf Hussain, Md. Ezharul Islam

Abstract: This research presents a comprehensive exploration of diverse microservices decomposition techniques. This research identifies the sequential steps integral to each decomposition method through a meticulous study and analysis of multiple techniques. Moreover, the paper integrates insights gleaned from a select group of experts. These experts offer valuable perspectives on software characteristics and elucidate the types of example software ideally suited for distinct decomposition types. They also validate the time and cost implications associated with each decomposition technique. Drawing from these multifaceted insights, the paper culminates in creating an algorithm. This algorithm is intricately designed based on collective knowledge and discussions surrounding software traits, such as suitability, time, and cost considerations linked to various decomposition techniques. This algorithm helps developers choose the most effective decomposition approach for microservices.

DOI: https://doi.org/10.5281/zenodo.17645806

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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.

Published by: vikaspatanker

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

Published by: vikaspatanker