Daily Archives: April 16, 2026

Authors: Sree Vishal G, Sarika K, Dr. K. Geetha

Abstract: The rapid advancement of digital technologies and the widespread use of social media platforms have significantly influenced the way individuals present themselves in modern society. Personal appearance, grooming, and aesthetic presentation have become essential aspects of self-expression and identity. However, selecting appropriate styles, outfits, and visual themes that align with individual preferences and current trends remains a complex and time-consuming task. Traditional approaches rely heavily on manual browsing, personal judgment, and external opinions, which often lack accuracy, consistency, and personalization.To address these challenges, this paper presents Aesthetiq, an Artificial Intelligence-based aesthetic analysis and personalized styling recommendation system. The proposed system is designed as a web-based application that leverages machine learning techniques to analyze user inputs such as facial images, style preferences, and visual attributes. The system performs preprocessing, feature extraction, and classification to identify suitable aesthetic categories and generate personalized recommendations.The architecture of the system consists of a frontend interface for user interaction, a backend server for processing and communication, and an AI module for performing analysis. The database stores user data, input images, and analysis results to enable efficient retrieval and history tracking. The system ensures real-time processing and provides visually interpretable outputs through an interactive dashboard.Experimental evaluation indicates that the proposed system achieves improved accuracy and performance compared to traditional methods. The system enhances user decision-making, reduces effort, and provides tailored recommendations that align with individual preferences and modern trends.

DOI: https://doi.org/10.5281/zenodo.19614962

Published by: vikaspatanker

Authors: MD Jaynul Abedin, Md Tayef Shiham

Abstract: Controlled-environment agriculture is rapidly becoming data-intensive and cyber-physical with the rapid digitalization of controlled-environment greenhouses. With artificial intelligence, IoT frameworks, and robotic surveillance systems becoming integrated in greenhouse operations, algorithms are playing a larger role in the managerial decision-making process instead of human supervisors alone. This change opens the idea of algorithmic management to the world of agricultural workforce – a field that has not been sufficiently investigated in the existing studies. This paper constructs a socio-technical system to examine the effect of the algorithm systems on workforce scheduling, performance tracking, and coordination of operations in the greenhouse environment. An optimization model in mathematics is presented to structure task distribution based on efficiency, fairness, and worker fatigue where multi-objective scheduling can be used to achieve productivity and human well-being. The paper offers a proposed structured simulation dataset and a survey instrument to help assess worker perceptions of surveillance and autonomy and fairness to support future empirical research. A comparative analysis of traditional and algorithmic management models indicates that there are trade-offs between agency and precision of operations and labor. The results emphasize that algorithmic management in the agricultural sector is not an issue of technical improvement but a governance problem that needs to be transparent, accountable, and human-centered. This study forms a conceptual and analytical base of ethically responsible AI-based workforce management in smart greenhouse settings and adds to the discussion on the future of human-AI collaboration in industrial systems.

DOI: https://doi.org/10.5281/zenodo.19614456

 

Published by: vikaspatanker

Authors: S.Senthazhai, V.Kokila, R.Dharshini, B.Pragathi, E.Sonashriyaa

Abstract: This paper presents the design and implementation of a smart environmental monitoring and control system using the Raspberry Pi Pico W microcontroller. The proposed architecture integrates multiple sensors—including temperature and humidity, gas level, water level, and feeder level—to continuously monitor ambient conditions. A forecasting module enhances system intelligence by predicting short-term environmental trends based on real-time data. The Raspberry Pi Pico W processes sensor inputs and communicates wirelessly with a cloud database, enabling remote access via mobile or desktop interfaces. Relay-controlled actuators such as a heater, cooling fan, exhaust fan, water pump, and servo motor respond dynamically to sensor thresholds, ensuring automated regulation of the environment. The system demonstrates a scalable and cost-effective solution for applications in smart agriculture, pet care, and automated home ecosystems. Experimental results validate the system’s responsiveness and reliability, highlighting its potential for real-world deployment in IoT-based automation frameworks.

DOI: https://doi.org/10.5281/zenodo.19608972

 

Published by: vikaspatanker

Authors: Dr. Jyoti Prakash

Abstract: Ruminant animals have a highly specialized microbial ecosystem within their rumen, allowing for the digestion of complex plant material such as cellulose. This mutualistic relationship not only provides for the nutritional requirements of the host animal but is also essential for ecosystem functioning. The rumen microbes play a large part in carbon and nitrogen cycling, but as a byproduct of anaerobic fermentation, methane is produced (Moss et al., 2000). Although methane production is a concern for global warming, ruminant animals are essential for the production of nutrient-dense foods from low-quality feedstuffs. This article will discuss rumen microbial ecology, its importance for ecosystem functioning, its contribution to climate change, and its importance for sustainable agriculture.

DOI: https://doi.org/10.5281/zenodo.19607530

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Authors: Sandeep Kumar Kashyap, Shweta Vikram

Abstract: High-precision GNSS Real-Time Kinematic (RTK) positioning often suffers from gross errors caused by non-line-of- sight (NLOS) multipath and other anomalies, which can dramatically bias simple coordinate averages. This paper presents Entropic-Topological Barycentric Synthesis (ETBS), a novel framework that dynamically selects a reliable subset of GNSS coordinates and computes a weighted barycentric average. The method proceeds in phases: (1) Topological filtering of the raw point set using kernel density estimation to identify and remove outliers; (2) Entropy weighting of remaining points based on multiple quality metrics (e.g. carrier-to-noise ratio, PDOP, satellite elevation variability) to assign higher weight to more reliable observations; and (3) Barycentric coordinate synthesis by computing the Wasserstein (transport) barycenter of the weighted points, yielding the final coordinate estimate. In synthetic tests mimicking open-sky and harsh urban conditions, ETBS consistently isolates outliers and yields centimeter-level accuracy, whereas traditional mean/median or robust least-squares methods produce errors on the order of decimeters or more. The results demonstrate that ETBS effectively neutralizes extreme outliers and achieves superior positioning precision.

DOI: https://doi.org/10.5281/zenodo.19606990

Published by: vikaspatanker

Authors: Ms. Babita, Dr. Brij Mohan Goel

Abstract: Cancer will continue to be a leading cause of mortality worldwide, making early detection and timely intervention essential for improving survival rates. This study will propose a hybrid Artificial Intelligence (AI)-based healthcare framework for early cancer detection and preventive analysis using deep learning techniques. The model will integrate Convolutional Neural Networks (CNN) for medical image feature extraction and Long Short-Term Memory (LSTM) networks for analyzing sequential clinical data.The system will be evaluated on benchmark cancer datasets using performance metrics such as accuracy, precision, recall, and F1-score. The proposed hybrid model is expected to outperform traditional machine learning approaches by achieving higher accuracy and lower error rates.The framework will support early-stage diagnosis, risk prediction, and personalized preventive strategies. Although challenges such as computational complexity and data privacy will persist, the proposed system is anticipated to offer strong potential for real-world healthcare applications and contribute to AI-driven cancer care.

Published by: vikaspatanker

Authors: Navipriyaa M, Pooja Ponrani D, Prathiba Devi V S, Mr. Prasannavenkatesan K

Abstract: Penetration testing plays a vital role in identifying security weaknesses in modern computing systems. With the rapid growth of distributed architectures, cloud-native applications, and microservices, traditional penetration testing approaches have become increasingly complex and time-consuming. Although automated tools are widely used, they typically function in isolation and require significant human expertise to coordinate multi-stage attack scenarios This paper presents an enhanced AI-driven penetration testing framework that leverages the Model Context Protocol (MCP) for structured communication and orchestration among multiple intelligent agents. The proposed system integrates reconnaissance, vulnerability assessment, exploitation, privilege escalation, and reporting into a cohesive pipeline. Unlike traditional systems, the framework incorporates contextual reasoning, adaptive decision-making, and dynamic exploit chaining using an AI Planner. Additionally, the system constructs real-time attack graphs and computes risk scores based on vulnerability severity, exploit confidence, and attack depth. Experimental results demonstrate significant improvements in automation efficiency, reduction in manual effort, and higher success rates in identifying complex exploit chains. The proposed framework represents a shift from static automation toward intelligent, adaptive penetration testing systems.

Published by: vikaspatanker

Authors: Shradha Kumavat, Kapil Shah

Abstract: Image restoration the task of recovering degraded or damaged images has become essential across many technical domains, including space imaging, medical imaging, and several post-processing applications. Most restoration techniques begin by modeling the degradation process that corrupts an image, typically involving blur and noise, and then attempt to reconstruct an approximation of the original image. However, in real-world scenarios, degradation is often unknown, requiring the simultaneous estimation of both the true image and the blurring function directly from the observed degraded image, without relying on prior knowledge of the blur mechanism. This thesis proposes a novel digital image restoration approach based on punctual kriging, supported by multiple machine learning algorithms. The work focuses on restoring images corrupted by Gaussian noise by achieving an effective trade-off between two competing goals: producing smooth, visually pleasing results while preserving edge details and structural integrity.

Published by: vikaspatanker

Authors: Priya Yadav

Abstract: Wireless Sensor Networks (WSNs) have become an essential technology for monitoring and data collection in various applications such as environmental monitoring, smart cities, healthcare systems, industrial automation, and military surveillance. These networks consist of numerous sensor nodes that are deployed in remote locations and operate with limited battery power. Since replacing or recharging batteries is often difficult, efficient power management becomes a crucial factor in ensuring the long-term operation of wireless sensor networks. Power management strategies aim to reduce energy consumption while maintaining network performance and reliability. This review paper presents a classification of major power management strategies used in wireless sensor networks and analyzes their performance based on key parameters such as energy consumption, network lifetime, scalability, and reliability. The study categorizes power management techniques into duty-cycling mechanisms, transmission power control methods, energy harvesting techniques, and energy-efficient routing approaches. Furthermore, recent advancements such as artificial intelligence-based energy optimization and adaptive power management techniques are discussed. The paper provides insights into current research trends and identifies future research directions for improving energy efficiency in wireless sensor networks.

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Published by: vikaspatanker

Authors: Mr Gipson M, Assistant Professor Ms. Bushra B

Abstract: Artificial Intelligence (AI) has emerged as a transformative force reshaping modern businesses across industries. It enables automation, enhances decision-making, and improves customer experience. This study examines how AI is transforming business operations, marketing strategies, and organizational efficiency. A descriptive research design was used, and data was collected through structured questionnaires and supported by secondary data sources. The findings reveal that AI significantly increases productivity, reduces operational costs, and enhances customer satisfaction. Businesses that adopt AI gain a competitive advantage in the market. However, challenges such as high implementation cost, lack of skilled workforce, and ethical concerns still exist. The study concludes that effective AI adoption strategies are essential for sustainable business growth.

DOI: http://doi.org/

Published by: vikaspatanker