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Journal articles on the topic 'Placement Prediction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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2

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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11

Kirandeep, Kirandeep, and Prof Neena Madan. "Deployment of ID3 decision tree algorithm for placement prediction." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 740–44. http://dx.doi.org/10.31142/ijtsrd11073.

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C.S Mohammed Asif and Dr.C.Gulzar. "Campus Placements Prediction & Analysis using Machine Learning." international journal of engineering technology and management sciences 9, no. 2 (2025): 797–800. https://doi.org/10.46647/ijetms.2025.v09i02.101.

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Placement of students is one of the most important objective of an educational institution. Reputation and yearly admissions of an institution invariably depend on the placements it provides it students with. That is why all the institutions, arduously, strive to strengthen their placement department so as to improve their institution on a whole. Any assistance in this particular area will have a positive impact on an institution’s ability to place its students. This will always be helpful to both the students, as well as the institution. In this study, the objective is to analyse previous yea

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Smitha, Kurian, Dev Krishnakant, Khan Laraib, Maaz Ahmad Md, and Taukir Md. "Survey on Placement Management, Prediction And Recommendation System." Journal of Advancement in Parallel Computing 6, no. 1 (2023): 27–35. https://doi.org/10.5281/zenodo.7739722.

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<em>Prediction of the performance of students is a matter of concern to the education institutions. The main purpose of placement prediction to know the chances of students getting placed in various companies. The role of placement management system computerizes the manual system and making the job easy for everyone. Students doesn’t know their chances of getting placed and the areas that they have to improve to get placed. There are several classification algorithm and mathematics-based techniques which can be used to classify the student’s information. Naive Bayes, SVM, KNN, Deci

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Al-Hyari, Abeer, Hannah Szentimrey, Ahmed Shamli, Timothy Martin, Gary Gréwal, and Shawki Areibi. "A Deep Learning Framework to Predict Routability for FPGA Circuit Placement." ACM Transactions on Reconfigurable Technology and Systems 14, no. 3 (2021): 1–28. http://dx.doi.org/10.1145/3465373.

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The ability to accurately and efficiently estimate the routability of a circuit based on its placement is one of the most challenging and difficult tasks in the Field Programmable Gate Array (FPGA) flow. In this article, we present a novel, deep learning framework based on a Convolutional Neural Network (CNN) model for predicting the routability of a placement. Since the performance of the CNN model is strongly dependent on the hyper-parameters selected for the model, we perform an exhaustive parameter tuning that significantly improves the model’s performance and we also avoid overfitting the

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Landgraff, Nancy C., Susan L. Whitney, Diane Wrisley, and Jamie Berlin. "Physical Therapist Prediction Accuracy of Discharge Placement from Acute Care." Stroke 32, suppl_1 (2001): 380–81. http://dx.doi.org/10.1161/str.32.suppl_1.380-e.

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P228 Background: The clinical impression of the Physical Therapist is requested in the determination of patient placement after an acute stroke. There is little evidence to determine if physical therapists are accurately making these recommendations. Also, there is little consensus regarding factors physical therapists consider when making these decisions. It is unknown if years of clinical experience affects placement judgment. The purpose of this retrospective chart review was to address the following questions: How accurate were the physical therapists in predicting placement and did experi

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Gupta, Sahil, Sourabh, Rounak Kumar, Sourav Raj, Dr Vishal Shrivastava, and Dr Devesh Kumar Bandil. "ML-Based: Placement Prediction Application." International Journal of Emerging Science and Engineering 13, no. 6 (2025): 20–25. https://doi.org/10.35940/ijese.f2603.13060525.

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This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engineering methods and advanced hyperparamet

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Sahil, Gupta. "ML-Based: Placement Prediction Application." International Journal of Emerging Science and Engineering (IJESE) 13, no. 6 (2025): 20–25. https://doi.org/10.35940/ijese.F2603.13060525.

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<strong>Abstract:</strong> This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engine

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Sahil, Gupta. "ML-Based: Placement Prediction Application." International Journal of Emerging Science and Engineering (IJESE) 13, no. 6 (2025): 20–25. https://doi.org/10.35940/ijese.F2603.13060525/.

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<strong>Abstract:</strong> This research paper examines machine learning models in predicting student placement outcomes in technical education. Given the increasing focus on employability in higher education, institutions need strong predictive models to improve placement readiness. We perform a stringent comparison of four sophisticated machine learning methods—Random Forest, XGBoost, Logistic Regression with Regularisation, and Support Vector Machines with RBF Kernel—on a complete dataset involving academic, technical, and behavioral metrics. Our approach requires feature engine

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Shaik, Alfana, Kodanda Rama Jammalamadaka Sastry, Chandra Prakash Vudatha, and Tirapathi Reddy Burramukku. "Cognitive and academic-based probability models for predicting campus placements." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1239–51. https://doi.org/10.11591/ijai.v11.i4.pp1239-1251.

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Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models ha

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20

Subhash, Ambika Rani. "Student Campus Placement Prediction Analysis using ChiSquared Test on Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 427–34. http://dx.doi.org/10.22214/ijraset.2021.37368.

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Every higher education institute aims to provide the best career opportunities for their students as part of the outcome based education system. In India, campus placements for students while pursuing their 4th year of engineering is a predominant factor since the reputation of any institute largely depends on reputed recruiting companies visiting campus and the number of placement offers being given to eligible students. Hence, campuses offer personality development training to their students just before the commencement of the placement season while students try to maintain a minimum CGPA wh

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21

Basil, Gladrene Sheena. "Prognostication of the placement of students applying machine learning algorithms." BOHR International Journal of Internet of things, Artificial Intelligence and Machine Learning 2, no. 1 (2023): 26–30. http://dx.doi.org/10.54646/bijiam.2023.14.

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Placement is the process of connecting the selected candidate with the employer. Every student might have a dream of having a job offer when he or she is about to complete her course. All educational institutions aim at having their students well placed in good organizations. The reputation of any institution depends on the placement of its students. Hence, many institutions try hard to have a good placement cell. Classification using machine learning may be utilized to retrieve data from the student-databases. A prediction model that can foretell the eligibility of the students based on their

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Alfana, Shaik, Sastry Kodanda Rama Jammalamadaka, Vudatha Chandra Prakash, and Burramukku Tirapathi Reddy. "Cognitive and academic-based probability models for predicting campus placements." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1239. http://dx.doi.org/10.11591/ijai.v11.i4.pp1239-1251.

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<span>Industrial organizations select the students for placement by conducting tests based on the academic content and targeting students' cognitive levels, such as the problem-solving ability. Educational institutes are mostly dependent on the students' academic performance to judge the likelihood of Employing the students. Cognitive and academic-based models are required to accurately predict the students' employment and assess the areas of improvement required. The interrelationships must be established to achieve coherence between the models. In this paper, three predictive models ha

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23

Ghume, Sahil Dileep, Yash Kishor Tambat, Mihir Arvind Sutar, Ashish Balkrishna Vartak, and Sakshi Janardan Kanar. "Campus Training Management and Placement Eligibility Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43601.

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The Campus Training Management and Placement Eligibility Prediction system enhances student training and placement processes through advanced technologies and data-driven insights. This project addresses inefficiencies in student record management, training progress tracking, and placement prediction by integrating academic performance, acquired skills, and extracurricular involvement. Leveraging machine learning, the system employs classification algorithm Random Forest to predict student placement outcomes. Feature selection is based on academic records, technical expertise, and soft skills.

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Asif, Sumaira, and Tabrez Nafis. "Students Placement Prediction Using Classification Techniques." International Journal of Computer Sciences and Engineering 7, no. 4 (2019): 289–93. http://dx.doi.org/10.26438/ijcse/v7i4.289293.

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Gandhi, Krishna, Aadesh Dalvi, Aniket Walse, and Sachin Bhoite. "Expert System for Student Placement Prediction." International Journal of Computer Applications Technology and Research 8, no. 9 (2019): 389–93. http://dx.doi.org/10.7753/ijcatr0809.1011.

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Srinivas, Mr C. K. "Students Placement Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 2771–74. http://dx.doi.org/10.22214/ijraset.2020.5466.

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27

Shrestha, Raj Man, Mehmet A. Orgun, and Peter Busch. "Offer acceptance prediction of academic placement." Neural Computing and Applications 27, no. 8 (2015): 2351–68. http://dx.doi.org/10.1007/s00521-015-2085-7.

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Majid, Ansari Maaz. "PLACEMENT PREDICTION SYSTEM USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30075.

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Engineering students are not sure what they want to study after graduation. Students are confused by the many options offered by universities such as postgraduate admissions, and factors such as salaries and different jobs worsen the situation. There is no reliable platform that allows students to predict outcomes from the beginning of engineering and take action to bridge the gap and create a better future. Students studying in engineering faculties need to know where they stand compared to others and what kind of placement they will get. Training and workshops are available when students ent

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Tiwari, Shiv Kumar. "A Hybrid Machine Learning Model for Predicting Engineering Student Placement with Explainable AI Techniques." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46561.

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Abstract—— Prediction of the placement of students is one of the most critical activities for training organizations to optimize training programs and curricula based on industry demands. This paper suggests a Hybrid Prediction Model for placement prediction of engineering students based on recent Machine Learning (ML) techniques. It employs various ML strategies such as Decision Trees, Random Forest, Support Vector Machines (SVM), and Gradient Boosting with ensemble learning for achieving maximum accuracy and reliability of prediction. It uses a large Kaggle dataset for testing and training f

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Milind Ruparel. "Enhancing Student Placement Predictions with Advanced Machine Learning Techniques." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 275–88. https://doi.org/10.52783/jisem.v10i1s.121.

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Optimal management of student placement mechanisms is pivotal to cost-effective distribution and individualized aid for learning establishments. The study presents a novel ensemble methodology to anticipate the outcomes of student placements, integrating manifold machine learning (ML) algorithms — logistic regression, naive Bayes, gradient boosting, linear discriminant analysis (LDA), k-nearest neighbours (KNN), random forest, and support vector machines (SVM). The data set has been constructed with an extensive scope covering various attributes from demographic details through socioeconomic s

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Wu, Xudong. "Research on Cigarette Market Capacity Forecasting Based on Data Mining Methods." Transactions on Economics, Business and Management Research 11 (November 5, 2024): 519–27. https://doi.org/10.62051/28e4gx03.

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With the tobacco industry gradually stepping into the modern cigarette marketing mode of data and informationization, cigarette precision marketing will become the industry's new way of refined marketing management. Among them, precise placement is one of the important contents of cigarette precision marketing. Precision placement is based on the segmentation, quantification, and combination of brand, customer, market, and time, aiming at precisely supplying the brand to the corresponding market segments, and realizing the goal of “finding the market for the brand, finding the brand for the ma

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Castano, Juan Alejandro, Zhibin Li, Chengxu Zhou, Nikos Tsagarakis, and Darwin Caldwell. "Dynamic and Reactive Walking for Humanoid Robots Based on Foot Placement Control." International Journal of Humanoid Robotics 13, no. 02 (2016): 1550041. http://dx.doi.org/10.1142/s0219843615500413.

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This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated f

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Geibel, Martin, and Galih Bangga. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches." Energies 15, no. 10 (2022): 3773. http://dx.doi.org/10.3390/en15103773.

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Data driven approaches are utilized for optimal sensor placement as well as for velocity prediction of wind turbine wakes. In this work, several methods are investigated for suitability in the clustering analysis and for predicting the time history of the flow field. The studies start by applying a proper orthogonal decomposition (POD) technique to extract the dynamics of the flow. This is followed by evaluations of different hyperparameters of the clustering and machine learning algorithms as well as their impacts on the prediction accuracy. Two test cases are considered: (1) the wake of a cy

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Mandrelia, Kanishk, Rahul Chauhan, Ananta Pandey, and Anam Khan. "Student Placement and Job Role Predictions." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 520–25. https://doi.org/10.22214/ijraset.2025.68288.

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Abstract: Career selection remains a critical challenge for students, influenced by external pressures and insufficient guidance. This study proposes a machine learning-based job role prediction system to address career uncertainty. Leveraging algorithms like Random Forest, Decision Tree, and SVM, the model analyzes academic performance, skills, interests, and personality traits from a dataset of 20,000 records. Data preprocessing included handling missing values, categorical encoding, and feature scaling, while Recursive Feature Elimination identified key predictors. Hyperparameter tuning via

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Veras, Lucas, Daniela Oliveira, Florêncio Diniz-Sousa, et al. "External Validation of Accelerometry-Based Mechanical Loading Prediction Equations." Applied Sciences 14, no. 22 (2024): 10292. http://dx.doi.org/10.3390/app142210292.

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Accurately predicting physical activity-associated mechanical loading is crucial for developing and monitoring exercise interventions that improve bone health. While accelerometer-based prediction equations offer a promising solution, their external validity across different populations and activity contexts remains unclear. This study aimed to validate existing mechanical loading prediction equations by applying them to a sample and testing conditions distinct from the original validation studies. A convenience sample of 49 adults performed walking, running, and jumping activities on a force

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Priya, T. Kavi, and N. Kumar. "Enhanced Butterfly Optimization and Deep Learning Algorithm for Student Placement Prediction." International Transactions on Electrical Engineering and Computer Science 4, no. 2 (2025): 91–102. https://doi.org/10.62760/iteecs.4.2.2025.139.

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Campus Placement (CP) is regarded in India as a crucial factor in determining universities or college's ranking and recognition. A university's standing and reputation are greatly influenced by the number of students it places in jobs and the average compensation offered to those students. It would be extremely beneficial to develop Deep Learning (DL)-based algorithms that can assist individuals in getting placement guidance, analyses labor market trends, and help educational institutions evaluate opportunities and expanding fields. Numerous realistic and potential placement criteria, such as

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Hu, Jieping, Yue Yu, Wei Liu, Jialei Zhong, Xiaochen Zhou, and Haibo Xi. "CT-Based Predictor for the Success of 12/14-Fr Ureteral Access Sheath Placement." International Journal of Clinical Practice 2022 (November 2, 2022): 1–7. http://dx.doi.org/10.1155/2022/3343244.

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Purpose. Ureteral access sheaths (UAS) are widely used in retrograde intrarenal surgery (RIRS), and this study aimed to develop a model for predicting the success of UAS placement based on computed tomography. Methods. We analyzed the clinical data of 847 patients who received ureteroscopy. Data on patient and stone characteristics and several computed tomography (CT)-based measurements were collected. A nomogram predicting the success of UAS placement was developed and validated using R software. Results. Two hundred and forty-seven patients were identified. Twenty-five patients (10.1%) faile

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Sun, Jiaxing, Shuaibo Wang, Zhenghao Wang, and Lixia Ji. "EDP placement model based on Python data prediction." Journal of Physics: Conference Series 1774, no. 1 (2021): 012040. http://dx.doi.org/10.1088/1742-6596/1774/1/012040.

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Kumar Pal, Ajay, and Saurabh Pal. "Classification Model of Prediction for Placement of Students." International Journal of Modern Education and Computer Science 5, no. 11 (2013): 49–56. http://dx.doi.org/10.5815/ijmecs.2013.11.07.

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Manjunath Mamulpet, Madhurya. "PUBG WINNER PLACEMENT PREDICTION USING ARTIFICIAL NEURAL NETWORK." International Journal of Engineering Applied Sciences and Technology 3, no. 12 (2019): 107–18. http://dx.doi.org/10.33564/ijeast.2019.v03i12.020.

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Jiménez, Daniel A. "Code placement for improving dynamic branch prediction accuracy." ACM SIGPLAN Notices 40, no. 6 (2005): 107–16. http://dx.doi.org/10.1145/1064978.1065025.

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Fan, Wenping, Puhui Meng, Yu Tian, Min-Ling Zhang, and Yao Zhang. "Adaptive VDI Session Placement via User Logoff Prediction." Machine Intelligence Research 22, no. 1 (2025): 189–200. https://doi.org/10.1007/s11633-023-1468-y.

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B M, Prof Ms Ganga, Mr Shivabasayya B. Hiremath, Mr Prasad V, Mr Sagar B, and Mr Supreeth S. "Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance." International Journal of Research Publication and Reviews 4, no. 3 (2023): 320–25. http://dx.doi.org/10.55248/gengpi.2023.31864.

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Meidisa Akhmad, Adinda, Ratna Farida Soenarto, Aldy Heriwardito, and Eloisa Nathania. "The Accuracy of Yoon’s Formula for Predicting Central Venous Catheter Depth in Indonesian Pediatric CHD Patients: A Cross Sectional Study." Majalah Anestesia & Critical Care 43, no. 1 (2025): 28–36. https://doi.org/10.55497/majanestcricar.v43i1.419.

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Introduction: A central venous catheter is a routinely inserted tool by anesthesiologists in open-heart surgery. However, incorrect central venous catheter placement depth may lead to complications or suboptimal usage. Yoon’s research in 2006 was done in paediatrics with congenital heart disease in Asia and developed a prediction formula for the depth of central venous catheter. This study aims to prove if Yoon’s formula can be applied to pediatric patients with congenital heart disease in Indonesia. Methods: This analytic observational study, with a cross-sectional design, involved 38 patient

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Suwa, Tohru, and Hamid Hadim. "Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Boards." Journal of Electronic Packaging 129, no. 1 (2006): 90–97. http://dx.doi.org/10.1115/1.2429715.

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A multidisciplinary placement optimization methodology for heat generating electronic components on printed circuit boards (PCBs) is presented. The methodology includes thermal, electrical, and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location which are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology which is based on a combination of a superposition method and artificial neural networks is developed for this study. Two genetic algorithms with

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Priyanka Singla, Vishal Verma. "An Improved Prediction Model for the Placement of the Students Considering Various Job Aspects." Journal of Information Systems Engineering and Management 10, no. 15s (2025): 708–17. https://doi.org/10.52783/jisem.v10i15s.2508.

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As the job market transforms, the placement of the students become very important with regard to career progression on an individual level as well as an institutional level. In the traditional placement models, the primary focus remains upon the academic grades while assigning very low or negligible importance to aspects like internships, soft skills, additional activities, industry certifications, and even job preferences. This work applies machine learning methods towards the development of a model designed to predict successful student placement achievement based on multiple features. This

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Kuś, Wacław, Waldemar Mucha, and Iyasu Tafese Jiregna. "Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios." Applied Sciences 15, no. 13 (2025): 7463. https://doi.org/10.3390/app15137463.

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This paper focuses on optimizing sensor placement in structures for load monitoring applications. Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. The goal is to maximize load monitoring accuracy under multiple loading scenarios while the number of sensors is kept smaller than the number of load cases. Building on prior work in which machine learning models predicted loads using only sensor readings without information on load location, this study continues that approach. It demonstrates that prediction models perfo

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Cao, Peng, Zhi Li, and Wenjie Ding. "A GNN-Based Placement Optimization Guidance Framework by Physical and Timing Prediction." Electronics 14, no. 2 (2025): 329. https://doi.org/10.3390/electronics14020329.

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Placement is crucial in physical design flow with significant impact on later routability and ultimate manufacturability in terms of performance, power, and area (PPA), which may deviate from finding the optimal solution and/or lead to unnecessary iterations suffering from interleaved optimization steps and inaccurate PPA estimation. To solve this issue, we propose a physical- and timing-related placement optimization guidance framework which provides candidate gate sizing and buffer insertion solutions as well as a path group for potential violated paths based on graph neural networks (GNNs)

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Li, Shunlong, Huiming Yin, Zhonglong Li, Wencheng Xu, Yao Jin, and Shaoyang He. "Optimal sensor placement for cable force monitoring based on multioutput support vector regression model." Advances in Structural Engineering 21, no. 15 (2018): 2259–69. http://dx.doi.org/10.1177/1369433218772342.

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Cable force monitoring is an essential and critical part of structural health monitoring for cable-supported bridges. The quality of obtained information depends considerably on the number and location of limited sensors. The purpose of this article is to provide a method for optimal sensor placement for cable force monitoring in cable-supported bridges. Based on the spatial correlation between neighbouring or symmetrical cable forces, the structural information of non-monitored cables can be predicted by multioutput support vector regression models, established between monitored (input) and t

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Buzurovic, Ivan, Tarun K. Podder, and Yan Yu. "Prediction Control for Brachytherapy Robotic System." Journal of Robotics 2010 (2010): 1–10. http://dx.doi.org/10.1155/2010/581840.

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In contemporary brachytherapy procedure, needle placement at desired location is challenging due to a variety of reasons. We have designed and fabricated an image-guided robot-assisted brachytherapy system to improve the needle placement and seed delivery. In this article we have used two different predictive control strategies in order to investigate the needle insertion efficacy and system dynamics during prostate brachytherapy. First, we used neural network predictive control (NNPC) to predict an insertion force. The NNPC uses the linearized state-space model of the robotic system to predic

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