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                <text>Early strength of concrete amended with waste foundry sand - A potential for early open to traffic (EOT) pavements</text>
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                <text>asphalt.; compressive strength; concrete pavements; rigid pavements; super-plasticizer; Waste foundry sand</text>
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                <text>The most predominant and widely practiced methods for waste disposal are Landfill, Incineration, and composting. There is a scarcity of land for waste disposal and because of increasing land cost, recycling and utilization of industrial by-products and waste materials has become an attractive proposition to waste disposal. There are several types of industrial by-products and waste materials. The utilization of such materials in concrete not only decreases the overall cost of construction but also helps in reducing disposal concerns. One such industrial by-product is waste foundry sand (WFS). The annual production is about 3 million tons from different industries in India. In the metal casting process, foundry industries dispose of huge quantities of waste sand into landfills, causing a harmful impact on the environment. The silica-based spent foundry sands from iron, steel, and aluminum foundries are evaluated in the risk assessment. This paper mainly focuses on achieving concrete for EOT (Early Open to Traffic) rigid pavements with WFS along with the use of accelerator and super-plasticizer. Effects of WFS on concrete properties such as compressive strength and split tensile strength are presented. Two types of mix proportions were investigated in this study. FDOT (Florida Department of transportation) and IRC (Indian Road Congress) recommendations were adopted for mix proportions using 5% &amp;amp; 10% of WFS replaced partially for M-Sand. 1-day compressive strength for FDOT mix with 10% WFS was 30MPa &amp;amp; for IRC mix with 10%, WFS was 20?MPa. The 3-days strength for mixtures with 10% WFS was 45MPa &amp;amp; 47MPa for FDOT &amp;amp; IRC mix proportions, respectively. Though the strength decreased with the inclusion of WFS, the 1-day and 3-days strength achieved for mixtures with 10% WFS surpassed the minimum strength requirements as per the slab replacement guidelines. Normally the pavement will be open to traffic after three to four days of laying asphalt, this method of using foundry sand enables the pavements to be open to traffic inless than a day.   2023 Author(s).</text>
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                <text>Raichurkar P.; Kumar P.; Mulge B.; Pidathala D.; Parul P.</text>
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                <text>AIP Conference Proceedings, Vol-2763, No. 1</text>
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                <text>American Institute of Physics Inc.</text>
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                <text>&lt;a href="https://doi.org/10.1063/5.0167899" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1063/5.0167899&lt;/a&gt;
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                <text>ISSN: 0094243X</text>
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                <text>Raichurkar P., CHRIST (Deemed to Be University), Bangalore, 560074, India; Kumar P., CHRIST (Deemed to Be University), Bangalore, 560074, India; Mulge B., CHRIST (Deemed to Be University), Bangalore, 560074, India; Pidathala D., CHRIST (Deemed to Be University), Bangalore, 560074, India; Parul P., CHRIST (Deemed to Be University), Bangalore, 560074, India</text>
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                <text>Biju, Albin; Prasad, Smrity; Kalaivani, S.</text>
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                <text>Early Warning System for Engine Failure Detection in Aircraft Engines Using Machine Learning</text>
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                <text>01-01-2025</text>
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                <text>Proceedings of the IEEE International Conference Image Information Processing;pp.483-489</text>
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                <text>&lt;a href="https://doi.org/10.1109/ICIIP68302.2025.11346157" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICIIP68302.2025.11346157&lt;/a&gt; &lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/pages/publications/105033221650?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105033221650?origin=resultslist&lt;/a&gt;</text>
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                <text>Biju A., Christ University, Bangalore, India; Prasad S., Christ University, Bangalore, India; Kalaivani S., Christ University, Bangalore, India</text>
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                <text>Aviation has a problem with engine defects which are a major concern. Unforeseen causes might render them expensive on the ground and hazardous in the air. We present a system that signals when an aircraft engine is about to fail. Our AdvancedModelTrainer checks a collection of models - Random Forest, XGBoost, Gradient Boosting, LightGBM, Ridge, Lasso, ElasticNet, and a simple neural network - through a dataset of 10,000 engine cycles along with 25 engineered features. Hyperparameter tuning and Remaining Useful Life (RUL) metrics help to select the top two (Gradient Boosting and XGBoost, RMSE 39.99, R2=0.7715). A complete MLOps structure keeps an eye on the drift, initiates the retraining process, and sets up dashboards that are user-friendly for the mechanics. The system has detected on 1,433 new engines, 1,126 were classified as Safe, 106 as Warning, and 201 as Critical, which is indicating the coverage of 93.44The dataset used was completely anonymized in order to safeguard sensitive operational data and to not conflict with the aviation data privacy regulations.  2025 IEEE.</text>
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              <elementText elementTextId="273757">
                <text>aircraft engines; early warning; machine learning; MLOps; predictive maintenance; remaining useful life</text>
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            <name>Publisher</name>
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              <elementText elementTextId="273758">
                <text>Institute of Electrical and Electronics Engineers Inc.</text>
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                <text>ISSN: 2640074X; ISBN: 979-833155618-1;</text>
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                <text>English</text>
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                <text>Conference paper</text>
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            <description>Information about rights held in and over the resource</description>
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              <elementText elementTextId="273762">
                <text>Restricted Access; Hardcopy may be available in the library</text>
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          <element elementId="50">
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            <description>A name given to the resource</description>
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                <text>Early-Stage Cervical Cancer Detection via Ensemble Learning and Image Feature Integration</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="165759">
                <text>cervical cancer; Deep learning; DenseNet121; feature integration; K means; ResNet50</text>
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                <text>Cervical cancer ranks as the fourth most common malignancy worldwide and poses a significant threat, particularly in resource-constrained regions. Automated diagnostic approaches, leveraging colposcope image analysis, hold great promise in curbing the impact of this disease. In this study, we introduce an ensemble of machine learning and deep learning models, including DenseNet 121, ResNet 50, and XGBoost to classify the cervical intraepithelial neoplasia. A novel feature integration is proposed which ensembles the results of the individual models in five fold validation process. Our methodology is deployed on a dataset sourced from the International Agency for Cancer Research. The results from the proposed framework have shown to be accurate, robust and dependable. This method can be utilized for achieving automatic identification of cervical cancer in early stages so it can be treated appropriately.  The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.</text>
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                <text>Mukku L.; Thomas J.</text>
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                <text>Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 112-122.</text>
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                <text>Springer Science and Business Media Deutschland GmbH</text>
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                <text>2024-01-01</text>
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                <text>&lt;a href="https://doi.org/10.1007/978-3-031-64813-7_13" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-3-031-64813-7_13&lt;/a&gt;
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                <text>ISSN: 23673370; ISBN: 978-303164812-0</text>
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                <text>English</text>
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                <text>Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Earthquake and flood resilience through spatial Planning in the complex urban system</text>
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                <text>Disaster resilience; Focus group discussion; Participatory approach; Spatial planning; Urban risk</text>
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                <text>Urban Communities are exposed to different disaster risks. The paper aims at understanding the interrelation of spatial planning and the resilience of the urban communities for earthquakes and floods. Various spatial planning components were used to evaluate the community resilience to earthquake and flood in the city of Pune of Maharashtra state in India. It has been identified that spatial planning contributes to a greater extent in determining community resilience. Spatial planning results in differential resilience among communities. In the study area, economically weaker households are found to be more vulnerable to disaster risk due to their spatial locations and limited accessibility to share the resources. These factors are found to be contributing to reduced resilience in the city.  2022 The Authors</text>
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                <text>Krishnamoorthy R., Department of Information and Communications Technology, Villa College QI Campus, Rah Dhebai Hingun, Male, 20373, Maldives; Robert N.R., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Latheef U.A., Department of Information and Communications Technology, Villa College QI Campus, Rah Dhebai Hingun, Male, 20373, Maldives</text>
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                <text>ISSN: 2537222</text>
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                <text>Jayagopalan G., Department of English, Christ University, India</text>
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                <text>Ecc-based secure group communication in energy-efficient unequal clustered wsn (Eeuc-ecc)</text>
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                <text>Elliptic curve cryptography (ECC) Secure group communication (SGC); Unequal clustering; Wireless sensor network (WSN); WSN multicasting</text>
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                <text>With an advent of the Internet of things (IoT), wireless sensor networks (WSNs) are gaining popularity in application areas like smart cities, body area sensor networks, industrial process control, and habitat and environment monitoring. Since these networks are exposed to various attacks like node compromise attack, DoS attacks, etc., the need for secured communication is evident. We present an updated survey on various secure group communication (SGC) schemes and evaluate their performance in terms of space and computational complexity. We also propose a novel technique for secure and scalable group communication that performs better compared with existing approaches.  Springer Nature Singapore Pte Ltd. 2020.</text>
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                <text>Vikram G.R., Department of CSE, Vignan Institute of Technology &amp;amp; Science, Hyderabad, Telangana, India; Krishna A.V.N., Department of CSE, Faculty of Engineering, CHRIST(Deemed to be University), Bangalore, India; Chatrapati K.S., Department of CSE, J.N.T.U.H College of Engineering-Manthani, Karimnagar, Telangana, India</text>
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                <text>eccentric completion; eccentric graphs; Eccentricity; iterated eccentric graphs</text>
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                <text>Raja M.R., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Mangam T.A., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India; Naduvath S., Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India</text>
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      </elementSet>
    </elementSetContainer>
  </item>
</itemContainer>
