Authors: Byragoni Srinivas, N.Sriaknth

Abstract: This project gives in brief, the theory behind the design of liquid retaining structure. Water tanks are storage containers for storing water. Elevated water tanks are constructed in order to provide required head so that the water will flow under the influence of gravity, the construction practice of water tanks is as old as civilized man. The water tanks project has a great priority as it serves drinking water for huge population from major metropolitan cities to the small population living in towns and villages. The main aim of this project is to understand the behavior of elevated water tank by observing the results of Bending Moment, Shear Forces, Maximum Stress, and Maximum Displacement and Design by using BIM software.

 

 

Published by: vikaspatanker

Authors: Mrs. T.Swapna Sridevi, Peddireddy Pattabhi Rama Lingeswar

Abstract: The rapid growth of digital communication platforms has significantly increased the volume of SMS and email messages exchanged daily. While these technologies enhance connectivity and information sharing, they have also become primary channels for spam, phishing, and fraudulent activities. Spam messages not only cause inconvenience but also pose serious security and privacy risks to individuals and organizations. Therefore, developing an accurate and efficient automated spam detection system has become an essential requirement. This study proposes a robust machine learning framework for intelligent classification of spam and legitimate (ham) SMS and email messages using advanced text analytics techniques. The system incorporates comprehensive preprocessing methods, including text cleaning, tokenization, stop-word removal, and normalization, followed by feature extraction using techniques such as TF-IDF and word embeddings. Multiple machine learning algorithms, including Naïve Bayes, Support Vector Machines, Logistic Regression, Random Forest, and Gradient Boosting, are implemented and comparatively evaluated. To further enhance predictive performance, ensemble learning strategies are employed to combine the strengths of individual classifiers. Experimental results demonstrate that the proposed hybrid framework achieves high accuracy, precision, recall, and F1-score across benchmark datasets. The system effectively minimizes false positives and false negatives, thereby improving reliability in real-world applications. The proposed approach contributes to the development of scalable, intelligent, and adaptive spam filtering systems capable of handling evolving spam patterns in modern communication networks.

DOI: https://doi.org/10.5281/zenodo.19061522

Published by: vikaspatanker

Authors: Dr Y Subba Reddy, A Guru Jyotshna, K Deepthi, B Paramesh, D.Siva Ganga Keerthi

Abstract: Neuroplasticity, as the name suggests, refers to the brain's remarkable ability to reorganize itself by forming new connections throughout life. Neuroplasticity has been observed to be more active in early childhood, as the processes of synaptic pruning and myelination are more active during this period. Research has shown that environmental stimulation has a direct effect on the thickness of the cortex, as well as the dendritic branching patterns of the neurons. Functional magnetic resonance imaging has shown that the brains of adults have a lot of plasticity, which enables the brains to recover from injury as well as to learn new skills. The neuroplasticity framework has a lot of implications, especially in the field of educational psychology as well as rehabilitation medicine. Experimental results using crawled Tor URL datasets demonstrate that the proposed method achieves 97.50% accuracy, outperforming conventional CNN-based deep fingerprinting techniques. Further optimization is achieved by incorporating a BiGRU layer after LSTM, enabling bidirectional feature extraction and improving prediction performance to 97.86%. Performance metrics including precision, recall, F1-score, and confusion matrices confirm the enhanced effectiveness of this methodology for distinguishing normal and attack-type Tor services, providing a robust framework for secure network monitoring.

DOI: https://doi.org/10.5281/zenodo.19060572

Published by: vikaspatanker

Authors: Rohit Sunil Khedkara, Sharad Dhanvijay

Abstract: The escalating atmospheric CO₂ concentration and its contribution to global climate change have driven intensive research into carbon capture technologies. Ionic liquids (ILs) have emerged as promising alternatives to conventional amine-based absorbents, offering unique advantages including negligible vapor pressure, exceptional thermal stability, and tunable physicochemical properties through rational cation-anion design. This comprehensive review examines the full spectrum of ionic liquid applications in CO₂ capture, from fundamental absorption mechanisms to process-scale implementations. Physical absorption in conventional ILs, chemisorption in task-specific ILs incorporating amine, carboxylate, and amino acid functionalities, and IL-based mixed absorbents are systematically analyzed. Structure-property relationships governing CO₂ solubility—including the influence of cation alkyl chain length, anion basicity, and functional group incorporation—are critically evaluated against experimental and computational data. Supported ionic liquid membranes (SILMs) and ionic liquid-based mixed matrix membranes for CO₂ separation are reviewed, highlighting permeability-selectivity trade-offs and stability considerations. Process configurations including IL-based absorption-desorption cycles, membrane contactors, and hybrid systems are assessed for energy consumption and economic viability. Recent advances in computational screening, machine learning-guided IL design, and process intensification are presented. Key challenges including high viscosity, long-term stability under operating conditions, absorbent regeneration energy, and scale-up economics are addressed. Finally, future directions toward industrial implementation are discussed, emphasizing the integration of ILs with renewable energy sources and the development of sustainable, cost-effective capture technologies.

DOI: https://doi.org/10.5281/zenodo.19050263

 

Published by: vikaspatanker

Authors: Won Kuk Jin, Choe Jin Hyok

Abstract: Window design is a critical factor significantly influencing building aesthetics, daylighting performance, visual comfort, and energy consumption. Conventional energy-saving strategies often rely on reducing window area, which negatively impacts architectural aesthetics and user satisfaction. This study proposes a novel form-oriented design approach that enhances energy efficiency while maintaining the window area. Four window geometries—flat, polygonal, protruded, and curved—were compared under identical area and material conditions. Key performance indicators included U-value, Solar Heat Gain Coefficient (SHGC), cooling and heating loads, and daylighting performance. The analysis revealed that curved windows achieved the highest cooling performance with an 18.2% reduction in cooling load but exhibited a significant drawback with an 8.2% increase in heating load, indicating substantial winter heat loss. Protruded windows showed a minimal cooling load reduction of only 0.3% and a 3.6% increase in heating load. Polygonal windows demonstrated the most balanced performance, with a 7.1% reduction in cooling load and a 3.8% increase in heating load. These results suggest that in a cold climate like Pyongyang, winter heating performance has a greater impact on annual energy consumption than summer cooling performance, implying that window form selection should not be based solely on summer performance.

DOI: https://doi.org/10.5281/zenodo.19049364

Published by: vikaspatanker

Authors: Devansh Indrodiya, Shivangi Patel

Abstract: The integration of Large Language Models (LLMs) into modern search engines has significantly transformed digital discoverability, shifting search behavior from deterministic webpage ranking to probabilistic entity citation within AI-generated responses. Unlike traditional search engines that present ordered lists of hyperlinks, generative search systems synthesize contextual answers and selectively cite businesses based on semantic relevance, trust signals, review sentiment, and inferred user intent. This transformation challenges conventional Search Engine Optimization (SEO) strategies that were originally designed to optimize positional ranking rather than inclusion within generative responses. This paper introduces Generative Engine Optimization (GEO), a geospatial artificial intelligence framework designed to model, measure, and improve business visibility in generative search environments. The proposed framework integrates geospatial analysis, semantic entity recognition, and machine learning–based prediction models to evaluate discoverability within AI-generated responses. A monitoring system called GeoRank360 is developed to track business citations across multiple generative platforms and compute a unified metric termed the Generative Visibility Score (GVS), which incorporates citation frequency, semantic prominence, sentiment strength, entity consistency, and temporal stability. An empirical evaluation conducted across 100 local businesses, five generative search platforms, 500 query variations, and over 4,000 geo-grid coordinates reveals spatial visibility volatility ranging from 35% to 60%, substantially higher than fluctuations observed in traditional search rankings. Predictive modeling achieves up to 87.1% accuracy in forecasting generative citation outcomes. The results indicate that semantic relevance exerts greater influence than geographic proximity in determining visibility within generative search responses. The proposed GEO framework establishes a foundation for future research in generative search visibility modeling, semantic ranking analysis, and AI-driven local discovery systems.

Published by: vikaspatanker

Authors: G. Janaki, P. Sangeetha, S. Swetha

Abstract: A Diophantine triple is a set of three positive integer a,b, c such that the product of any two distinct elements is added to one, is a perfect square .This article investigates the existence of a specific Diophantine triple involving Centered Hexagonal Number ensuring the product of any two members of the added to the property D(n).

DOI: https://doi.org/10.5281/zenodo.19019965

Published by: vikaspatanker

Authors: Deepak Tomar, Kismat Chhillar

Abstract: Water resource management has become increasingly challenging due to rapid population growth, climate variability, urbanization, and rising agricultural demand. Traditional hydrological models often struggle to capture the complex and nonlinear interactions between environmental variables affecting water systems. Machine Learning (ML) offers powerful data-driven techniques that can analyze large and heterogeneous datasets to support efficient water management. This paper explores the role of machine learning in water resource management, highlighting its applications in hydrological forecasting, irrigation optimization, groundwater monitoring, and water quality assessment. Various ML algorithms such as Artificial Neural Networks, Random Forest, Support Vector Machines, and Deep Learning architectures are examined for their ability to model complex hydrological processes. The study also discusses current challenges including data availability, model interpretability, and integration with existing hydrological frameworks. The findings indicate that ML-based approaches can significantly enhance predictive accuracy, optimize resource utilization, and support sustainable water management strategies.

DOI: https://doi.org/10.5281/zenodo.19019143

Published by: vikaspatanker

Authors: Akalya M, Mohammed Suhail Akthar J, Rohan P S, Dr R Karthik

Abstract: In Order To Detect And Track Wildlife In Real Time, Computer Vision Techniques Are Being Used More And More In Wildlife Monitoring. Modern YOLO Object Detectors (Yolov3, Yolov4, Yolov5, Yolov7, And Yolov8) Combined With Multiobject Tracking Algorithms, Specifically SORT And Deepsort, Are Assessed And Contrasted In This Study For Automated Wildlife Monitoring. Wildlife Camera Trap Datasets Are Used To Evaluate These Models' Performance, Taking Into Account Metrics Like Tracking Accuracy, Precision, Recall, Mean Average Precision (Map), And Inference Speed.According To Experimental Results, Deepsort Considerably Increases Tracking Stability By Lowering Identity Switches Through Appearance-Based Association, While Yolov8 Achieves The Best Detection Performance In Terms Of Map And AP@0.5. When Paired With Deepsort, Yolov5 Offers A Robust, Lightweight Baseline That Achieves High Tracking Accuracy (MOTA ≈ 94%) While Utilizing Computational Power Efficiently. Conversely, SORT, Which Has More Identity Switches And Only Uses Motion Cues. The Results Show The Trade-Offs Among YOLO Variants In Terms Of Detection Accuracy, Model Size, And Computational Cost. The Suggested YOLO + Deepsort Framework Shows Great Promise For Real-Time Wildlife Monitoring On Edge Devices Like Uavs And Field Cameras, Supporting Applications Like Habitat Analysis, Biodiversity Assessment, Antipoaching Surveillance, And Mitigating Conflicts Between Humans And Wildlife.

DOI: https://doi.org/10.5281/zenodo.19018863

 

Published by: vikaspatanker

Authors: Aswetha. M, Danwin shaju, Mrs. Sangeetha Priya

Abstract: The volume of unstructured texts has increased dramatically in recent years due to the internet and the digitization of information and literature. This onslaught of data will only grow, and it will come from new and unusual sources. Thus, it will be necessary to develop new and inventive approaches and tools to process and make sense of this data. Investors in the financial markets can now get information faster than ever before thanks to the expansion of communication channels, in addition to the online availability of news and reports in text format through providers like Reuters and Bloomberg. This contains a plethora of information that is often overlooked by financial market data. In order to measure the sentiment of a text, predictive and deductive methods are applied, these methods aim at extrapolating new feautures from big data.

DOI: https://doi.org/10.5281/zenodo.19018551

 

Published by: vikaspatanker