Category Archives: Uncategorized

Authors: Dr. R.K. Dubey, Mohd. Wahid Mansury

Abstract: This study analyzes the effects of the cosmological constant  in the context of the Bianchi Type I cosmological model. The Bianchi Type I model represents an anisotropic but spatially that universe, where expansion rates can differ along three spatial directions. The cosmological, plays a significant role in the universe expansion. This work aims to understand how influences the expansion, energy density, and anisotropy of the universe. Einstein's field equations with variable cosmological constant if considered in the presence of a perfect fluid for a Bianchi type I universe by assuming that the cosmological term is proportional to the square of the Hubble parameter. The variation law for vacum density was recently proposed by many researches on the basis of the quantum field estimation in a curved expanding background. The cosmological term tends asymptotically to a genuine cosmological constant and the model tends to a de- Sitter universe. More obtained some new results by using a slightly different method from that of other researchers obtained the result that the present universe is accelerating with a large fraction of cosmological density in the form of a cosmological term.

Published by: vikaspatanker

Authors: Dr. R Sridhar

Abstract: – It is increasingly important for high-rise buildings in urban environments to consider blast threats arising from explosions. When blast loads produce high-amplitude and short-duration pressure pulses, they primarily pose a threat to the facade systems, which are at risk from high-rise blowouts. It could also cause harmful reactions over the world and the gradual breakdown of weak systems. The state-of-the-art in semi-active structural control techniques (particularly magnetorheological (MR) dampers) and passive protective measures (glazing, cladding, sandwich panels, anchorage/retention systems) is summarised in this review. Hybrid design approaches that integrate adaptive damping and material resilience are also examined. In order to identify critical research gaps (scaling of semi-active devices, controller latency for impulsive loads, long-term durability, and standards integration), we survey empirical blast test programs, standards and guidance documents, numerical modelling approaches, controller algorithms, and device technologies. We then suggest a prioritised research agenda that includes system-level testing, predictive control research, and demonstration projects.

Published by: vikaspatanker

Authors: Mr Shaikh Aslam Amir, Ms Khalate Vaishanavi Suresh, Shaikh Karishma Mohammad, Malve Somnath Bhanudas

Abstract: This project introduces a smart environmental monitoring and alert system built using IoT technology. The system uses a NodeMCU (ESP8266) microcontroller connected to three important sensors: the DHT11 for measuring temperature and humidity, a Flame Sensor for detecting fire, and an MQ6 Gas Sensor for identifying dangerous gases like LPG and propane.Data from these sensors is collected in real time and sent to the Blynk IoT platform, where it can be viewed on a mobile app. The system also includes a Telegram Bot API that sends immediate alerts to the user’s Telegram account when dangerous situations occur, such as high temperature, gas leaks, or fire detection. This ensures that users are informed quickly, even if they are not physically present near the sensors. The system is affordable, easy to expand, and suitable for home safety, industrial use, and remote monitoring.Using IoT platforms and instant messaging services, this system provides a practical method for smart alerts and real-time detection of environmental dangers

DOI: http://doi.org/10.5281/zenodo.17191043

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Authors: Varshini J S, Praveen, Keshav Acharya. P, Rakesh, Rohit. S

Abstract: This work presents a personal assistant robot designed to minimize human labor in daily activities. Operated by voice command, it features a camera, robotic arm, object detection, and distance measurement, making it suitable for various applications, including chemical industries and healthcare. This scoping review sought to comprehend individuals' experiences using humanoid robots to perform daily living activities. studies were studied, and nine robots to perform different tasks were identified. The majority of participants found the robots safe and convenient but didn't like their size and slowness. Others found the robots fascinating but not appropriate for domestic use. The results indicate the necessity of tailored research to enhance the performance of humanoid robots in health care.

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Authors: Vanaja Kumari Degala

Abstract: Phishing attacks continue to pose a serious cybersecurity threat by exploiting social engineering techniques to deceive users into disclosing sensitive information. These attacks commonly rely on malicious Uniform Resource Locators (URLs), often hosted on newly registered or parked domains to evade traditional blacklist-based detection systems. Early identification of such URLs is essential to reduce financial losses and identity theft. This paper presents a machine learning–based framework for the early detection of malicious URLs, with particular emphasis on newly registered and parked domains. A dataset comprising 211,659 URLs was constructed using real-time SSL certificate monitoring, popular domain listings, and verified phishing reports. The proposed approach incorporates data preprocessing, URL-based feature extraction, class balancing, and model optimization. Experimental results demonstrate that the Light Gradient Boosting Machine (LGBM) classifier achieves a recall of 96.02% and an accuracy of 97.28% using 10-fold cross-validation. Feature selection techniques further reduce model complexity while maintaining detection performance, enabling practical deployment. The framework provides a proactive and scalable solution for phishing prevention and brand protection in sectors such as banking and e-commerce.

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Authors: Bhavesh Vallepu

Abstract: Artificial Intelligence (AI) is transforming the global job market, reshaping industries, and redefining the nature of work. This paper explores the multifaceted impact of AI on employment, highlighting both the opportunities and challenges it presents. While AI drives efficiency and innovation, leading to the creation of new job categories and business models, it also poses a threat to certain traditional roles through automation and displacement. The analysis considers various sectors, skill levels, and geographical regions, emphasizing the need for adaptive education systems, upskilling, and policy intervention to manage the transition. By examining current trends and future projections, this study aims to provide a balanced perspective on how AI will influence employment dynamics in the years to come

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Authors: Ravichandra Mulpuri

Abstract: The growing demand for personalized, efficient, and secure customer interactions has accelerated the adoption of AI-enhanced Salesforce bots across industries. These bots integrate natural language processing, machine learning, and CRM intelligence to streamline engagement while adapting to user needs in real time. Their deployment in hybrid Unix environments provides enterprises with a balance of stability, scalability, and flexibility. However, ensuring compliance with global regulations such as GDPR, HIPAA, and PCI DSS remains a central challenge. This review explores the role of AI-powered Salesforce bots in enhancing customer experiences, examines compliance strategies within hybrid Unix systems, and highlights ethical, operational, and organizational considerations. Future directions emphasize the importance of compliance-by-design, secure integration, and industry-specific applications, positioning AI-driven bots as transformative tools in enterprise digital ecosystems.

DOI: http://doi.org/10.5281/zenodo.17189840

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Authors: Abdullah Tariq

Abstract: The evolution of iOS application development has witnessed a significant shift from traditional monolithic architectures to more sophisticated patterns that balance modularity with performance. This paper examines the concept of modular monoliths in large-scale iOS applications, exploring how this architectural pattern addresses the dual challenges of code reusability and runtime performance. Through analysis of implementation strategies, performance metrics, and real-world case studies, we demonstrate that modular monoliths offer a pragmatic middle ground between rigid monoliths and complex microservices architectures. Our findings suggest that when properly implemented, modular monoliths can achieve up to 40% better build times, 25% improved memory efficiency, and significantly enhanced developer productivity while maintaining the deployment simplicity of monolithic applications.

DOI: https://zenodo.org/records/17183546

Published by: vikaspatanker

Authors: Sangani Harshil, Kalariya Meet, Baraiya Ravi, Vasani Bhumil, Dr. Vikram B.Kaushik

Abstract: The ability to automatically describe visual content through natural language represents a compelling frontier in artificial intelligence research. Our work addresses this complex challenge by developing a sophisticated neural architecture that translates visual information into coherent textual descriptions. The methodology we employed centers on a two-stage approach: initially, we leverage the robust feature extraction capabilities of InceptionV3, a well-established convolutional neural network, to visual elements present in uploaded images. The extracted visual representations then feed into our custom language generation pipeline, built around a Gated Recurrent Unit (GRU) architec- ture. What distinguishes our implementation is the incorporation of a spatial attention module that enables selective focus across different image regions during the caption formation process. This attention-driven approach mirrors human visual processing, where we naturally emphasize certain areas while describing a scene. To validate the practical utility of our research, we constructed an intuitive web-based platform using Streamlit framework. This interactive system allows users to seamlessly up- load photographs and receive instantaneous caption generation, enhanced with audio narration capabilities through integrated speech synthesis technology.

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Authors: Praveen Lokanath

Abstract: The rise of artificial intelligence (AI) is reshaping the nature of work in ways that echo past industrial revolutions, yet with unprecedented speed and complexity. This paper explores how previous waves of technological transformation — from mechanization in the 18th century to the digital revolution of the late 20th century — can inform our understanding of AI's current and future impact on employment, labor markets, and workforce dynamics. Drawing lessons from history, the study highlights patterns of job displacement, creation, and evolution, emphasizing the critical roles of policy, education, and social adaptation. It also examines the unique characteristics of AI that distinguish it from earlier innovations, particularly its capacity to automate cognitive tasks and decision-making processes. By synthesizing historical insights and contemporary developments, the paper offers a framework for anticipating the challenges and opportunities AI presents, aiming to guide stakeholders in shaping a more equitable and resilient future of work

Published by: vikaspatanker