Daily Archives: April 30, 2026

Authors: Arghadeep Nath, Rajat Takkar

Abstract: Early-stage hiring processes continue to depend on resume-based and keyword-based filtering, which does not reliably capture a candidate’s actual abilities. This paper presents an AI-assisted skill evaluation system that prioritizes demonstrated performance over resume content. The system models candidate screening as a multi-stage pipeline: skill profiling, dynamic assessment delivery, automated rule-based and NLP evaluation, and weighted score aggregation. A competency model maps candidate skills to standardized assessment criteria, enabling objective cross-candidate comparison. Evaluation on simulated data (n=100) yields a Spearman rank correlation of 0.91, a false-positive shortlist rate of 12%, and a top-quintile precision of 78% — all substantially better than a conventional ATS baseline. The proposed framework is scalable, modular, and designed to reduce bias inherent in resume-centric screening.

Published by: vikaspatanker

Authors: Rishi Raghav Singh, Rohan Singh, Rajat Takkar

Abstract: More than 30% of worldwide deaths involve diseases caused by cardiovascular and lifestyle factors (WHO, 2023) As awareness of early risk identification advances, accessible practical screening tools for use in primary care continue to be either very expensive, reliant on specialists or both. In this paper, we propose a Multi-Modal AI-Based Health Intelligence Framework with an explicit focus on two interrelated concepts encapsulated in the form of two specialized individual modules: Disease Risk Assessment (DRA) module and Lifestyle Analysis (LSA) module. After systematic preprocessing and class-balancing, the DRA module trains LR, SVM, and RF on the Cleveland Heart Disease dataset (303 patients). The LSA module takes user-reported behavioral behaviors — BMI, physical activity, sleep, dietary quality, and stress — to calculate a composite Lifestyle Risk Index (LRI). Both modules are provided through a Streamlit web application that provides real time predictions with SHAP-based explanation. Amongst all the classifiers we evaluated, Random Forest performed best with a 91.8% accuracy, AUC-ROC = 0.956 It powers a sub 60 ms response time for the system and is deployable in the cloud.

Published by: vikaspatanker

Authors: Kasarla Vanitha, B.Archana

Abstract: Cloud computing has transformed the IT landscape by providing scalable, flexible, and cost-effective services. However, its multi-tenant infrastructure introduces significant security and privacy challenges due to shared resources and virtualized environments. This paper examines established security and privacy frameworks, threat models, and protective architectures designed to address these concerns. Through a comparative analysis of existing literature and technical frameworks, the study identifies key vulnerabilities and effective mitigation strategies in multi-tenant cloud environments. Additionally, graphical models and charts are included to demonstrate how shared resource access and virtualization can be secured using encryption, tenant isolation, and dynamic authentication mechanisms.

DOI: https://doi.org/10.5281/zenodo.19909008

Published by: vikaspatanker

Authors: Wasim Riyajoddin Kazi, Om Vitthal Devakate, Vishal Popatrao Jagadale, Kavita Shinde

Abstract: In recent years, the issues related to public safety and security have increased significantly, which resulted in a surge of demand for automated surveillance systems. However, traditional monitoring systems based on CCTV require constant human surveillance, which is not only wasteful but also error- prone. This paper proposes a deep learning-based surveillance system that can automatically detect suspicious activities in videos. The proposed model utilizes CNNs to classify video frames into normal and suspicious categories. Upon detection of suspicious activity, the system captures the frame and sends an automated email notification to the registered system administrator using the SMTP protocol. The proposed system utilizes OpenCV for video processing, TensorFlow/Keras for training and predicting the models, and SQLite to securely store administrator information within a database.

Published by: vikaspatanker

Authors: Prudvi Saisaran Ponduru

Abstract: Recent advances in foundation models, multimodal learning, reasoning-oriented large language models, agentic workflows, and edge AI have expanded the capabilities of artificial intelligence systems. However, practical decision-making remains brittle because many systems optimize prediction quality while under-modeling intervention effects, uncertainty, safety constraints, latency budgets, and human accountability. This paper introduces AEGIS-DM, an adaptive, edge-aware, governed, interventional, and safe decision-making framework designed for AI systems deployed across emerging technology settings including agentic assistants, cyber-physical systems, healthcare decision support, and enterprise automation. The framework combines five layers: multimodal state representation, predictive scoring, causal effect estimation, simulator- or planner-based long-horizon optimization, and a governance layer for calibration, fairness, policy checks, logging, and human override. We further propose a cross-domain evaluation protocol using public resources such as Adult, D4RL, WebShop, ALFWorld, MIMIC-IV Demo, NASA CMAPSS, and M5, together with open-source tooling including OpenAI Evals, Responsible AI Toolbox, OpenSpiel, RecSim NG, Stable-Baselines3, and RLlib. Because this manuscript is a methods-and-benchmark contribution, the quantitative section reports deterministic scenario-based simulation results under the stated protocol rather than production deployment measurements. Under the reference protocol, the proposed hybrid approach is expected to outperform rule-based, supervised-only, offline-RL-only, and prompt-only agent baselines in composite decision quality and robustness while maintaining substantially better latency and cost than cloud-only frontier-model pipelines.

DOI: https://doi.org/10.5281/zenodo.19968793

Published by: vikaspatanker