Gotowe bibliografie tematyczne / Placement Prediction

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Gotowa bibliografia na temat „Placement Prediction”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Placement Prediction"

G S, Parvathy. "A Placement Prediction System for Polytechnic Students." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3183–87. http://dx.doi.org/10.22214/ijraset.2024.59601.

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Abstract: The placement prediction system makes predictions about a candidate's likelihood of being hired based on several factors, including talents, backlogs, CGPA, and more. Here, previous placement information is analyzed to identify success parameters and create a machine-learning model that forecasts placement results in the future. It is designed to encourage students to improve their academic performance, enhance their skill set, and develop additional soft skills to increase their likelihood of securing successful job placements. This system guarantees that educational establishments

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Khamkar, Pratiksha, Rutuja Lagad, Priyanka Shinde, Shubhangi Londhe, and Prof S. S. Bhosale. "Students Placement Prediction System." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 784–86. http://dx.doi.org/10.22214/ijraset.2022.47448.

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Abstract: Placement of students is one in every of the vital activities in academic establishments. Admission and name of establishments primarily depends on placements. Hence all institutions strive to strengthen placement department. The main objective of this paper is to analyze previous year’s student’s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions. We are not going to consider the placement of students not only by their academic performances but also aptitude, technical and communication skills, an

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Jadhav, Prof Rupali, Sanket Shinde, Shubham Ghadge, Rushikesh Babar, and Anurag Bobde. "Student Placement Prediction Portal." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5536–41. http://dx.doi.org/10.22214/ijraset.2024.62896.

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Abstract: We have acquired knowledge through articles and papers on the use of machine learning to anticipate student placement. In our understanding of the education field, it is evident that placement holds importance, for both students and educational institutions. For students it can provide insights into their likelihood of securing placements enabling them to make informed decisions regarding their career paths. Although we are still in the development phase and continuously gaining insights into this matter, we are confident in our potential as a tool, in creating an accurate, reliable

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Srivastava, Vaibhav, Saksham Kaushik, and Ujjwal Raj. "Student Placement Package Prediction By Regression Analysis." International Journal of Research Publication and Reviews 5, no. 1 (2024): 4724–29. http://dx.doi.org/10.55248/gengpi.5.0124.0345.

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M, Mr Sreenivasa. "Placement Prediction System." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 700–702. http://dx.doi.org/10.22214/ijraset.2021.37458.

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Placement prediction system is a useful software for managers and students. An educational institution contains student records which is a wealth of information but is very large one person analyzes complete student records. To find out the placement status of each student at institution is a tedious task. Therefore, the limit of the system includes the use of time, which is minimal efficient and with little user satisfaction. The project implementation prediction plan predicts the reader placement using a variety of machine learning methods such as merging methods, regression strategies, deci

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Lu, Zhihui, Junchao Yang, Kuan Tao, Xiangxin Li, Haoqi Xu, and Junqiang Qiu. "Combined Impact of Heart Rate Sensor Placements with Respiratory Rate and Minute Ventilation on Oxygen Uptake Prediction." Sensors 24, no. 16 (2024): 5412. http://dx.doi.org/10.3390/s24165412.

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Oxygen uptake (V˙O2) is an essential metric for evaluating cardiopulmonary health and athletic performance, which can barely be directly measured. Heart rate (HR) is a prominent physiological indicator correlated with V˙O2 and is often used for indirect V˙O2 prediction. This study investigates the impact of HR placement on V˙O2 prediction accuracy by analyzing HR data combined with the respiratory rate (RESP) and minute ventilation (V˙E) from three anatomical locations: the chest; arm; and wrist. Twenty-eight healthy adults participated in incremental and constant workload cycling tests at var

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Likhitha, Penmetsa Sri Sai Jyothi, and Ms Mallarapu Poojitha. "Campus Placement Prediction And Analysis Using Machine Learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 12423–25. https://doi.org/10.55248/gengpi.6.0525.18153.

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Siddhesh Kadam and Prabha Siddhesh Kadam. "Machine Learning for Placement Prediction: A Study Using Weka, Orange, and Simple ML." Voice of Creative Research 7, no. 2 (2025): 190–96. https://doi.org/10.53032/tvcr/2025.v7n2.25.

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Placement prediction is a crucial application of machine learning in education, helping institutions and students understand employability factors. It enables educational institutions to design effective training programs and assists students in improving their career prospects. This study evaluates the performance of three popular data analysis tools, namely Weka, Orange, and Simple ML, using a publicly available placement dataset. The dataset comprises various features, including academic performance, extracurricular involvement, and technical skills, which play a significant role in determi

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Syawab, Moh Husnus, Yunifa Miftachul Arief, Fresy Nugroho, Ririen Kusumawati, Cahyo Crysdian, and Agung Teguh Wibowo Almais. "Optimizing Goods Placement in Logistics Transportation using Machine Learning Algorithms based on Delivery Data." Jurnal ELTIKOM 8, no. 2 (2024): 201–9. https://doi.org/10.31961/eltikom.v8i2.1321.

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This study addresses the challenge of predicting the optimal placement of goods for expeditionary transportation. Efficient placement is crucial to ensure that goods are transported in a manner that maximizes space and minimizes the risk of damage. This study aims to develop a prediction system using the K-Nearest Neighbor (KNN) method, which is based on expert data from expedition vehicles. To evaluate the effectiveness of the KNN method, the researcher compared it with the Support Vector Machine (SVM) method. By doing so, they sought to determine which method delivers more accurate predictio

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Journal, IJSREM. "USING MACHINE LEARNING FOR CAMPUS PLACEMENT PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27027.

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Abstract— In the modern world, campus placements play a crucial role in shaping the career trajectories of students and determining the reputation of educational institutions. This study addresses the growing need for accurate and predictive tools in campus placement through the application of machine learning (ML) models. We focus on widely-used ML algorithms that have emerged as powerful tools for predicting fitting technical fields. The research utilizes a comprehensive dataset that incorporates diverse features, including academic performance for technical proficiency. By leveraging these

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Rozprawy doktorskie na temat "Placement Prediction"

LaCurts, Katrina L. (Katrina Leigh). "Application workload prediction and placement in cloud computing systems." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91039.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. 97 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 128-135). Cloud computing has become popular in recent years. Companies such as Amazon and Microsoft host large datacenters of networked machines available for users to rent. These users are varied: from individual r

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Chang, Shih-Yen. "Placement of measurement points for wear-out prediction with regard to electromigration." Thesis, Linköping University, ESLAB - Embedded Systems Laboratory, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57751.

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Nowadays, electronic systems are widely used in applications such as mobile phones, laptops, etc., but the electronic systems are not permanent and indestructible, so the reliability of an electronic system is a major concern. However, the lifetime of electronic systems are shorter than it was 40 years ago because the dimensions of wires are smaller due to the advanced manufacturing technologies. Electromigration is a wear-out mechanism which becomes an important issue, due to the fact that the reduced dimension of wires makes current density increase so that, the probability

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Hörmann, Philipp [Verfasser]. "Thermoset automated fibre placement – on steering effects and their prediction / Philipp Hörmann." München : Verlag Dr. Hut, 2016. http://d-nb.info/1084387093/34.

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Delbruel, Stéphane. "Towards an architecture for tag-based predictive placement in distributed storage systems." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S006/document.

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Cette thèse rassemble et présente la part plus importante de mes recherches durant ces dernières années, portant sur les systèmes de stockage décentralisés. Dans cette thèse, nous affirmons que les méta-données générées par les utilisateurs dans les systèmes pleinement distribués de gestion de contenu généré par les utilisateurs représentent une source d’informations fiable nous permettant de déterminer lors de la mise en ligne d’un contenu, où il sera consommé dans le futur. Cela rend donc possible le placement prédictif de contenus proche de ses futurs consommateurs, augmentant par là même l

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Trundle, Jennifer. "Manipulating the placement of error: the effect of prediction of accuracy in function learning /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19217.pdf.

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Sanders, Karen Nordberg. "The Role of a Developmental Screening in Kindergarten - First Grade Placement." Thesis, North Texas State University, 1986. https://digital.library.unt.edu/ark:/67531/metadc331090/.

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The purposes of this study were to determine if a kindergartener's developmental stage correlates with subsequent scholastic achievement, to determine whether developmentally younger children who repeat kindergarten attain higher academic achievement than developmental 1y younger children who do not repeat kindergarten, and to investigate the relationship between head circumference, developmental age, and achievement. Ninety-seven kindergartners of various ethnicity and socio-economic status were administered the Gesell School Readiness Screening Test to determine developmental age and were f

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Haehl, Martha Barger Rita. "Placement test scores and demographics predicting grades in Basic Math and Introductory Algebra at an urban/suburban community college." Diss., UMK access, 2007.

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Thesis (Ph. D.)--School of Education. University of Missouri--Kansas City, 2007. "A dissertation in education and urban leadership and policy studies in education." Advisor: Rita Barger. Typescript. Vita. Title from "catalog record" of the print edition Description based on contents viewed July 30, 2008. Includes bibliographical references (leaves 81-87). Online version of the print edition.

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Kim, Dae Hyun. "Through-silicon-via-aware prediction and physical design for multi-granularity 3D integrated circuits." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43642.

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The main objective of this research is to predict the wirelength, area, delay, and power of multi-granularity three-dimensional integrated circuits (3D ICs), to develop physical design methodologies and algorithms for the design of multi-granularity 3D ICs, and to investigate the impact of through-silicon vias (TSVs) on the quality of 3D ICs. This dissertation supports these objectives by addressing six research topics. The first pertains to analytical models that predict the interconnects of multi-granularity 3D ICs, and the second focuses on the development of analytical models of the capaci

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Zakaria, Sakirah. "A comparison between Mathematics Placement Examination and ACT mathematics on certain classes of students at Kansas State University." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9984.

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Ryan, Michael F. "The effects of length of placement and parental contact on institutionalized juvenile offenders' self concepts." CSUSB ScholarWorks, 1986. https://scholarworks.lib.csusb.edu/etd-project/387.

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Książki na temat "Placement Prediction"

Noble, Julie. Differential prediction/impact in course placement for ethnic and gender groups. American College Testing Program, 1996.

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Bridgeman, Brent. Prediction of grades in college mathematics courses as a component of the placement validity of SAT-mathematics scores. College Entrance Examination Board, 1989.

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Noble, Julie. Differential prediction/impact in course placement for ethnic and gender groups Julie Noble, Jill Crouse, Matt Schulz. American College Testing Program, 1996.

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Camara, Wayne J. Using the PSAT/NMSQT and course grades in predicting success in the Advanced Placement Program. College Entrance Examination Board, 1998.

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Andrews, Martha. Deconstructing volunteerism: The differences between variables predicting student placements and volunteerism activities. Laurentian University, School of Graduate Studies, 2003.

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Denny, George. Kindergarten report card comments as indicators of future pupil placement. 1988.

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Klein, Craig A. Relationships between selected entry placement factors and performance on the College Level Academic Skills test. 1992.

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Kohn, Robert, Jessica Warner, Wendy Verhoek-Oftedahl, and Emily Murphy. Elder Abuse. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199374656.003.0021.

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There are five principal domains of elder abuse: physical abuse, psychological abuse, sexual abuse, neglect, and financial abuse. This chapter discusses the prevalence, prediction, and assessment of elder abuse as well as other factors related to abuse. The incidence of elder abuse is 24 times greater than the number of cases referred to social service, law enforcement, or other legal authorities. Caregiver factors, rather than risk factors associated with the abused elder, may be more important in predicting abuse. Lack of compliance with medical regimens, delay in seeking medical care, dispa

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Jones, Jack, and Larry K. Britt. Design and Appraisal of Hydraulic Fractures. Society of Petroleum EngineersRichardson, Texas, USA, 2009. http://dx.doi.org/10.2118/9781555631437.

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Using an interdisciplinary approach, Design and Appraisal of Hydraulic Fractures offers a basic yet comprehensive introduction to the completion and reservoir engineering aspects of hydraulic fracture stimulation. The book is divided into three sections. Section 1 covers the design and placement of a hydraulic fracture stimulation; topics include the basics of the hydraulic fracturing process, stress issues, fracture geometry, controls on generated length and width, fluid and proppant selection, quality control, and quality assurance. Section 2 introduces the use of dynamic data to characteriz

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van Dulmen, Manfred H. M., and Haylee DeLuca. Former Foster Care Youth and Resilient Functioning in Young Adulthood. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190260637.003.0043.

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This chapter focuses on former foster care youth and resilient functioning in young adulthood, particularly predictors of success and future directions. The chapter reviews the literature on predictors of (a) work and educational competence, (b) competence in close relationships, and (c) self-competence. The chapter reviews empirical evidence for success components related to pre-placement, placement, individual, and contextual factors. The chapter concludes with five recommendations for future research: (1) strength-based developmental assessments of competence and resilience, (2) identificat

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Części książek na temat "Placement Prediction"

Bhargavi, Maridu, Kunal Kumar, G. Sai Vijay, Ch Sai Teja, and Neerukonda Dharmasai. "Student placement prediction." In Pedagogical Revelations and Emerging Trends. CRC Press, 2024. https://doi.org/10.1201/9781003587538-27.

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Joshi, Rushikesh, Vijay Shilwant, Roshan Dandge, Shreyash Rajput, and Manav Ashok Thakur. "“Campus Placement Prediction and Analysis”." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8526-1_36.

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Ashok, M. V., A. Apoorva, and V. Chethan. "Placement Chance Prediction: Clustering and Classification Approach." In Advances in Intelligent Systems and Computing. Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2656-7_25.

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Kumari, Khushi, Bipul Kumar, P. Jahnavi, and Y. Snehalatha. "Optimizing student placement prediction using ensemble learning." In Pedagogical Revelations and Emerging Trends. CRC Press, 2024. https://doi.org/10.1201/9781003587538-25.

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Sajwan, Samarth, Rudraksh Bhardwaj, Revaan Mishra, and Shruti Jaiswal. "Student Placement Prediction Using Machine Learning Algorithms." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4362-3_22.

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Udawat, Bharat, Advait Kale, Divit Sinha, Hardik Sharma, and Deepa Krishnan. "Campus Placement Prediction System Using Deep Neural Networks." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3071-2_3.

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Khayat, Kamal H., Ahmed F. Omran, and Matthew D’Ambrosia. "Prediction of SCC Formwork Pressure in Full-Scale Elements." In Design, Production and Placement of Self-Consolidating Concrete. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9664-7_20.

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Ferrara, Liberato, Nathan Tregger, and Surendra P. Shah. "Flow-Induced Fiber Orientation in SCSFRC: Monitoring and Prediction." In Design, Production and Placement of Self-Consolidating Concrete. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9664-7_35.

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Spangenberg, J., N. Roussel, J. H. Hattel, et al. "Prediction of the Impact of Flow-Induced Inhomogeneities in Self-Compacting Concrete (SCC)." In Design, Production and Placement of Self-Consolidating Concrete. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9664-7_18.

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Babu, Tina, Rekha R. Nair, M. Manjula, and Utso Bhattacharyya. "Effective placement prediction for educational institutions: Insights from machine learning." In Advances in Electrical and Computer Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003515470-12.

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Streszczenia konferencji na temat "Placement Prediction"

Maragatham, T., P. Yuvarani, S. Harishri, and R. Swetha. "Student Placement Prediction using Deep Learning Techniques." In 2024 8th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2024. https://doi.org/10.1109/iceca63461.2024.10800998.

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Kumar, A. Shiva, Ramana Gowrishetty, K. Samanvi, Shiva Sai Manikanta, and S. Uday Nishant. "Placement Prediction & Analysis Using Machine Learning." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11019060.

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Priyadarsini, Jukta, Veena S. Badiger, Sheetal, and Anitha DSouza Jacintha. "Placement Prediction Using the Artificial Neural Network (ANN)." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915275.

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Bodke, Minal, Vishnu Tiwari, Jayesh Wadhwani, Trapti Gorbade, and Rohan Soundane. "A Review of Various Deep Learning Approaches for Placement Prediction." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968796.

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G, Ragu, Kiran M, and Koushik H. "Comparative Study of Explainable AI Techniques for Student Placement Prediction." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020370.

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Máth, Benedek, Ádám Nemes, and Balázs Varga. "Iterative GNN-Based Traffic Flow Prediction and Sensor Placement Optimization." In 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2025. https://doi.org/10.1109/saci66288.2025.11030106.

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Kireet, M., R. Suneetha Rani, Pathan Firoze Khan, B. Lalitha, Shaik Johny Basha, and Pithani Nagamani. "Campus Placement Prediction using Facebook Prophet and eXtreme Gradient Boost Algorithm." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717090.

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Subha, P., K. Jayashree, R. Prabavathi, S. Karppakavalli, B. Deepa, and G. Lavanya. "Placeseries'O – Placement Preparation with Placement Prediction System." In 2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS). IEEE, 2023. http://dx.doi.org/10.1109/iccebs58601.2023.10448980.

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Markov, Igor. "Session details: Session 4: Wirelength Prediction for Placement." In SLIP03: International Workshop on System Level Interconnect Prediction. ACM, 2003. http://dx.doi.org/10.1145/3248306.

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Chow, Wing-Kai, Jian Kuang, Peishan Tu, and Evangeline F. Y. Young. "Fence-aware detailed-routability driven placement." In 2017 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP). IEEE, 2017. http://dx.doi.org/10.1109/slip.2017.7974905.

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Raporty organizacyjne na temat "Placement Prediction"

Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement typ

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Ungar, Eugene D., Montague W. Demment, Uri M. Peiper, Emilio A. Laca, and Mario Gutman. The Prediction of Daily Intake in Grazing Cattle Using Methodologies, Models and Experiments that Integrate Pasture Structure and Ingestive Behavior. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568789.bard.

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This project addressed the prediction of daily intake in grazing cattle using methodologies, models and experiments that integrate pasture structure and ingestive behavior. The broad objective was to develop concepts of optimal foraging that predicted ingestive behavior and instantaneous intake rate in single and multi-patch environments and extend them to the greater scales of time and space required to predict daily intake. Specific objectives included: to determine how sward structure affects the shape of patch depletion curves, to determine if the basic components of ingestive behavior of

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Carey, Ashley, Madeleine Roberson, Isaac Howard, and Jameson Shannon. Toward a method to predict thermo-mechanical properties of high-strength concrete placements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49660.

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In this article, the merits of a thermo-mechanical framework to estimate properties of high-strength concrete are evaluated for potential standardization as a test method. Previous work conducted by the authors was summarized to show the individual advancements toward development of a laboratory testing framework. Most notably, laboratory-based curing protocols have been shown to produce temperature profiles that were similar to mass placements and achieving peak temperatures that were within 2°C of peak temperatures recorded in a mass high-strength concrete placement. Additionally, current te

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Howard, Isaac, Thomas Allard, Ashley Carey, Matthew Priddy, Alta Knizley, and Jameson Shannon. Development of CORPS-STIF 1.0 with application to ultra-high performance concrete (UHPC). Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40440.

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This report introduces the first release of CORPS-STIF (Concrete Observations Repository and Predictive Software – Structural and Thermodynamical Integrated Framework). CORPS-STIF is envisioned to be used as a tool to optimize material constituents and geometries of mass concrete placements specifically for ultra-high performance concretes (UHPCs). An observations repository (OR) containing results of 649 mechanical property tests and 10 thermodynamical tests were recorded to be used as inputs for current and future releases. A thermodynamical integrated framework (TIF) was developed where the

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Welp, Timothy, Brian Harris, Brian McFall, et al. Development and testing of the Sediment Distribution Pipe (SDP) : a pragmatic tool for wetland nourishment. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48411.

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Standard dredging operations during thin layer placement (TLP) projects are labor intensive as crews are necessary to periodically move the outfall location, which can have lasting adverse effects on the marsh surface. In an effort to increase efficiency during TLP, a novel Sediment Distribution Pipe (SDP) system was investigated. This system offers multiple discharge points along the pipeline to increase the sediment distribution while reducing pipeline movements. An SDP Modeling Application (SDPMA) was developed to assist in the design of SDP field applications by quickly assessing the press

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add

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Heath, Samantha, Sue Hudson, Nasyitah Abd Aziz, et al. Stocktake of placement preparation and clinical experience for Aotearoa New Zealand student nurses in aged care settings: July 2023. Unitec ePress, 2023. http://dx.doi.org/10.34074/rsrp.103.

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The global population is getting older. In 2019 there were 703 million people aged ≥65 years. This figure is predicted to double to 1.5 billion, or 16% of the global population, by 2050 (United Nations, 2020). By comparison, Aotearoa New Zealand is expected to see a 36% increase in people aged ≥65 years for the ten years between 2021 and 2031 (Stats NZ, 2020). Since age is the most significant predictor for the most common health conditions, and the prevalence of chronic diseases and disability is also set to increase, pressures on most healthcare services are likely to escalate (Inouye et al.

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Brydie, Dr James, Dr Alireza Jafari, and Stephanie Trottier. PR-487-143727-R01 Modelling and Simulation of Subsurface Fluid Migration from Small Pipeline Leaks. Pipeline Research Council International, Inc. (PRCI), 2017. http://dx.doi.org/10.55274/r0011025.

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The dispersion and migration behavior of hydrocarbon products leaking at low rates (i.e. 1bbl/day and 10 bbl/day) from a pipeline have been studied using a combination of experimental leakage tests and numerical simulations. The focus of this study was to determine the influence of subsurface engineered boundaries associated with the trench walls, and the presence of a water table, upon the leakage behavior of a range of hydrocarbon products. The project numerically modelled three products including diesel, diluted bitumen (dilbit) and gasoline; which were chosen to span a range of fluid types

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Turner, John. Evaluation of Elongation Criteria and Friction Loss in Ground Anchors. Deep Foundations Institute, 2014. http://dx.doi.org/10.37308/cpf-2013-soil-1.

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This report describes a research project for evaluating the applicability of the widely accepted 80 percent criterion for elongation of ground anchors to anchors with unbonded lengths exceeding 100 feet. This issue is driven by several recent projects involving ground anchors for landslide stabilization in which a significant percentage of the anchors did not meet the criterion that requires measured elongation during proof load testing of at least 80 percent of the theoretical elastic elongation. The projects involved anchor unbonded lengths in the range of 85 to 220 feet, which is outside th

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Exploring Predictive Factors for Chicago Public Schools to Expand Advanced Placement Computer Science Principles. The Learning Partnership, 2023. http://dx.doi.org/10.51420/report.2023.3.

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