Showing 1-25 of 50,670 resultsfor "Index Terms":Performance Of Machine Learning Models
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Predicting Employee Performance using Machine Learning to Enhance Workforce Efficiency
Noureen Talpur;Nathaniel Chol Gakeer Alier;Hafizan Matsom;Mohd Hafizul Afifi Abdullah;Shazia Khatoon
2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA)
Year: 2024 | Conference Paper |
The aim of this study is to develop a machine learning model that utilizes historical employee data to predict future performance within organizational contexts. To achieve this, employees are categorized into three distinct groups: high performers, moderate performers, and low performers. The objective is to use this data to enhance talent management and decision-making in organizations. Building...Show More
Performance Evaluation Model using Unsupervised K-Means Clustering
Bolisetty Kanaka Durga Prasad;Bhaskar Choudhary;B Ankayarkanni.
2020 International Conference on Communication and Signal Processing (ICCSP)
Year: 2020 | Conference Paper |
Performance Appraisal is an evaluation process done by every company to analyze whether the goals and outlines of that company or organization have been met by the employees or not. It helps to evaluate each and every recruit in terms of their skills, knowledge, identify their strengths and weaknesses and gives them a report for future improvement. The organizations overall performance includes th...Show More
XGBoost for Novel Predictive Model to Classify Student Performance Based on Academic Metrics
2024 IEEE Silchar Subsection Conference (SILCON 2024)
Year: 2024 | Conference Paper |
Understanding and predicting student performance with academic metrics is essential for better education and learning. Making precise estimates, selecting appropriate measures, and using suitable predictive algorithms may be tricky. In this research, we investigate the significance of these measurements and predictive models. We built XGBoost predictive model to predict student performance. We dep...Show More
A Graphical User Interface for Fast Evaluation and Testing of Machine Learning Models Performance
Leonel Rosasn-Arias;Gabriel Sanchezn-Perez;Linda K. Toscano-Medina;Hector M. Perez-Meana;Jose Portillo-Portillo
2019 7th International Workshop on Biometrics and Forensics (IWBF)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
In this paper we propose the design of a graphical tool for fast evaluation of Machine Learning (ML) models performance in classification tasks. The motivation behind this work is to get some intuition on what machine learning model we can use to get the best possible outcome out of our datasets. The designed GUI allows us to decide whether applying data standardization and applying different data...Show More
Machine Learning-Based Prediction of MPI Communication Time in High-Performance Computing Applications
2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC)
Year: 2024 | Conference Paper |
The rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC systems and directly affects the performance and efficiency of these applications. Therefore, modeling and predicting the performance of MPI communication is critically important. Thi...Show More
A Novel Performance Evaluation Strategy of Automatic Machine Learning on Electricity Services
2022 5th International Conference on Data Science and Information Technology (DSIT)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
In electricity services, the customers have many different requests, and different solutions need to be designed for filling these demands. For example, the customer sentiment classification task and meter image classification task requires experts to design different models to solve them respectively. However, it is time comsuming to design a specific model for machine learning experts, which lar...Show More
Simulation of Machine Learning Techniques to Predict Academic Performance
2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Educational Data Mining (EDM) is that branch of Artificial Intelligence (AI) which uses a combination of Data Mining (DM) and Machine Learning (ML) techniques to make predictions on aspects specifically related to students, teachers, and, to educational institutions, in general. The goal of any educational institution is to ensure that every student gets the “right” foundational, high-quality educ...Show More
Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the ...Show More
Stacking Based Machine Learning Approach for Student Performance Analysis
2024 Global Conference on Communications and Information Technologies (GCCIT)
Year: 2024 | Conference Paper |
Students academic performance forms an important aspect in education. Students who are not performing well faces multiple problems like graduating after the scheduled time, or dropping out of college or universities. Thus, educational institutes should observe the performance of their students' closely so that they can focus on the students with bad or low performance in the early stages to avoid ...Show More
Performance Analysis of Supervised Machine Learning Algorithm for Prediction of Diabetes
2022 International Conference on Edge Computing and Applications (ICECAA)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Diabetes is affecting a lot of people nowadays. It is a very common disease throughout the world. People found it difficult to predict the probability of this disease at an earlier stage. The disease is not curable yet by medical science but it can only be controlled. Furthermore, patients of this disease have to take medicines throughout their life. This disease can be predicted using different s...Show More
Analyzing the effective factors influencing online learning performance is a research topic that has garnered significant attention. Traditional approaches, such as multiple regression and structural equation models, tend to assume linearity, while non-linear machine learning models lack interpretability. To address this gap, we propose a framework that interprets machine learning models to analyz...Show More
Machine Learning Technique to Analyze the Health Condition of Athletes and Predict their Performance
Sonu Jose;Agnes Teresa Maliackal;Aiswarya Sukumaran;Jofit Cyriac;Rincy Merin Varkey
2023 International Conference on Circuit Power and Computing Technologies (ICCPCT)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
In the past few years, the application of machine learning techniques in the field of sports science has gained considerable attention. The ability to analyse vast amounts of data and extract meaningful insights has opened up new opportunities for understanding the health conditions of athletes and predicting their performance. In this paper, we explore the use of machine learning techniques for a...Show More
Machine Learning-based Risk of Hospital Readmissions: Predicting Acute Readmissions within 30 Days of Discharge
Mirza Mansoor Baig;Ning Hua;Edmond Zhang;Reece Robinson;Delwyn Armstrong;Robyn Whittaker;Tom Robinson;Farhaan Mirza;Ehsan Ullah
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2019 | Conference Paper |
Cited by: Papers (6)
The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk of readmission models - LACE index and patient at-risk of hospital readmission (PARR). The study cohort consisted of 180,118 admissions with 22565 (12...Show More
Performance Evaluation of Machine Learning Algorithms for Stability Prediction in Smart Grids
M.L. N Vital;Srinu Ejjipurapu;Daya Sagar Battu;Apsha Mohammad;Uday Annaparedddy
2024 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES)
Year: 2024 | Conference Paper |
Cited by: Papers (2)
A smart grid represents an advanced electrical network that incorporates modern technology to efficiently manage electricity generation, distribution, and consumption. Majorly, a smart grid’s stability is affected by voltage fluctuations and frequency deviations due to power demand. Due to this, there is instability in the grid, fluctuations in the energy supply, and the need for real-time adjustm...Show More
Bias-Boosted ELM for Knowledge Transfer in Brain Emotional Learning for Time Series Forecasting
This paper presents the Bias-Boosted Extreme Learning Machine guided Brain Emotional Learning (B2ELM-BEL) model, a significant advancement in chaotic time series prediction that effectively incorporates knowledge transfer learning. Integrating traditional Brain Emotional Learning (BEL) with the novel Biased-ELM method, the B2ELM-BEL introduces a bias term into the output weights of Extreme Learnin...Show More
Performance Maintenance of Machine Learning-based Emergency Patient Mortality Predictive Models
2021 4th International Conference of Computer and Informatics Engineering (IC2IE)
Year: 2021 | Conference Paper |
Cited by: Papers (4)
Machine learning provides a flexible technique to predict the survival of patients who are admitted to hospital as emergency admissions. Mortality prediction is a central component of emergency patient quality of care and this can act as an indicator of severity to determine who needs prioritized care. Machine learning-based models, as opposed to human-crafted severity score systems, allow for muc...Show More
Employee Performance Measurement Using Machine Learning Algorithms
Vandana Madaan;Charn Singh;Indu Bharti Jain;Shilpa Wadhwa;Pradeepta Kumar Sarangi;Ashok Kumar Sahoo
2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG)
Year: 2024 | Conference Paper |
Employee performance measurement is a critical aspect of organizational management, aiming to assess and improve the productivity, efficiency, and effectiveness of employees. With the advancement of technology, machine learning (ML) has emerged as a powerful tool in enhancing how organizations measure and manage employee performance. This work implements three machine learning models such as Suppo...Show More
From Bricks to Bytes: The AI Transformation of Cybersecurity Firewalls
2024 4th International Conference on Advancement in Electronics & Communication Engineering (AECE)
Year: 2024 | Conference Paper |
Due to the frequent occurrence of network threats, robust intrusion detection systems (IDS) are essential for safeguarding digital data. Traditional rule-based IDS often struggle to address emerging cyber threats, underscoring the necessity for adaptable solutions. This study presents a Next Generation Firewall (NGFW), a firewall variant that incorporates machine learning (ML) and deep learning (D...Show More
A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces
Ryan G. L. Koh;Mafalda Ribeiro;Leen Jabban;Binying Fang;Karlo Nesovic;Sayeh Bayat;Benjamin W. Metcalfe
Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and have a broad range of applications including in bioelectronic medicine and neuroprostheses. They can modulate neural activity through stimulation or monitor conditions by recording from the peripheral nerves. The recent growth of Machine Learning (ML) has led to the application of a wide variety of M...Show More
Machine Learning Models for Aircraft Tail Performance & Degradation Predictions
Ramakrishnan Raman;Bindu R;Pradeep Mahalingaiah;Manish Goswami
2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)
Year: 2021 | Conference Paper |
Conventional aircraft performance models used in onboard avionics systems are based on aircraft state parameters, aircraft performance characteristics and engine performance characteristics. These performance characteristics are established as part of the aircraft design development lifecycle, and the associated models are fine-tuned empirically based on flight test data by OEMs, prior to the airc...Show More
Enhancing Team Sports Strategy and Performance Using Machine Learning: A Case Study on Soccer
K.Venkata Narayana;Nalli Vinaya Kumari;G. S. Pradeep Ghantasala;Rajesh Sharma R;U. Ananthanagu;Deepika Gupta
2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON)
Year: 2024 | Conference Paper |
This study aims to improve performance in team sports, with a specific focus on football, using machine learning techniques. The research explores different machine learning methods, such as Random Forest, Decision Tree, and Support Vector Machine (SVM), by analyzing a dataset that includes player statistics, match results, and in-game events. The Random Forest model performed the best, with 88% a...Show More
Diabetes Prediction using SMOTE and Machine Learning
Maganti Khyathi Sarayu;Shaik Ayesha Bhanu;Karanam Deekshitha;Maduri Meghana;Iwin Thanakumar Joseph
2024 Second International Conference on Inventive Computing and Informatics (ICICI)
Year: 2024 | Conference Paper |
This research work explores highly sophisticated diabetes prediction algorithms employing the PIMA Indian Diabetes dataset. Proposed research intends to explore the influence of model update, assessment criteria, and data preparation on prediction algorithms. In this extensive research, a pre-selected dataset coupled with feature scaling, stratified selection, and oversampling is employed to tackl...Show More
Applicability of Neural Network Based Models for Software Effort Estimation
2019 IEEE World Congress on Services (SERVICES)
Year: 2019 | Conference Paper |
Cited by: Papers (21)
Effort Estimation is a very challenging task in the software development life cycle. Inaccurate estimations may cause the client dissatisfaction and thereby, decrease the quality of the product. Considering the problem of software cost and effort prediction, it is conceivable to call attention to that the estimation procedure considers the qualities present in the data set, as well as the aspects ...Show More
Research on dynamic adaptation of English translation based on deep reinforcement learning
2024 International Conference on Artificial Intelligence, Deep Learning and Neural Networks (AIDLNN)
Year: 2024 | Conference Paper |
This research is dedicated to exploring the application of deep reinforcement learning (DRL) in dynamic adaptability of English translation. This study compared traditional sequence-to-sequence models, models using attention mechanisms, and advanced models integrating deep reinforcement learning to assess their impact on improving machine translation quality. The research found the deep reinforcem...Show More
Federated machine learning defines a machine learning framework that allows a collective model to be constructed from data that is distributed across repositories owned by different organizations or devices. A blueprint for data usage and model building across organizations and devices while meeting applicable privacy, security and regulatory requirements is provided in this guide. It defines the ...Show More
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