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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Osama Yassin Mohammed Ibrahim Ahmed Saleh dept. of Computer Science College of Computer and Mathematics University of Mosul Nineveh Iraq dept. of Software College of Computer Science and Mathematics University of Mosul Nineveh Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This paper proposes intelligence methods for the prediction of wireless network traffic and the evaluation of its development. This solution analyses the changing patterns of actual business traffic data in cellular networks in the time dimension and uses ensemble learning (EL) to predict the tendency of traffic, providing guidance for operators' network optimization deployment from a short-term perspective. The paper depends on a dataset taken from the Eurecom ElasticMon 5G monitoring platform. The approach applied a multi-dimensional mapping relationship between other operational data and traffic in the network using four ensembles of learning: Extreme Gradient Boosting (XGBoost), ADA boosting, gradient boosting algorithm, and a voting regression algorithm. They applied to find the upper limit of traffic that the cellular cell can carry, optimizing the deployment of the network from a long-term perspective. The results showed that XGBoost outperforms other methods, whose best performance ensures the privacy of traffic data.

关键词： Economics Data privacy Machine learning algorithms Wireless networks Telecommunication traffic prediction algorithms Boosting Ensemble learning Optimization Monitoring

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Research on Iron Core Loss prediction Based on Data-Driven and Machine Learning algorithms

Research on Iron Core Loss Prediction Based on Data-Driven a...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Hao Jin Lei Dong China University of Petroleum (East China) Qingdao China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper focuses on the performance optimization of magnetic components, and discusses the key issues such as the construction of a data-driven core loss prediction model. A data-driven core loss prediction model is constructed, which integrates a variety of machine learning algorithms, such as random forest, gradient lift tree, K-nearest neighbor regression, decision tree regression and artificial neural network, and improves the stability and accuracy of prediction through weighted prediction model. Finally, with the core loss prediction model as the objective function, particle swarm optimization algorithm was used to determine the optimization conditions of the magnetic components, including the optimal material, temperature, frequency, waveform and magnetic flux density peak, and the overall performance optimization of the magnetic components was realized.

关键词： Machine learning algorithms Magnetic cores Core loss Predictive models prediction algorithms Stability analysis Particle swarm optimization Optimization Thermal stability Regression tree analysis

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Machine Learning-Based Cardiovascular Disease prediction: A Comparative Analysis of Classification algorithms for Enhanced Diagnostic Accuracy

Machine Learning-Based Cardiovascular Disease Prediction: A ...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Harsheeta Singh Vanshaj Satija Priyadharshini K Department of Computing Technologies SRM Institute of Science and Technology Kattankulathar Chennai

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Cardiovascular diseases (CVDs) were one of the major causes of death in all the continent, to such an extent that early detection and accurate diagnosis of CVDs is crucial to the early intervention. The aim of this research is to predict the likely hood of a heart related condition based on ML techniques in order to increase diagnostic precision. To develop predictive models, a dataset of 383 patient records along with 13 key attributes such as age, cholesterol levels, blood pressure, heart rate was used. The implementation of various ML algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Naïve Bayes, and Logistic Regression in Python was done with the help of Jupyter Notebook environment. And the study focuses on data preprocessing, feature selection, and algorithm performance evaluation by means of accuracy, precision, recall, and F1-score. SVM was found to be the model with the highest accuracy of 86% on the tested models, which shows its ability to cope with the complexity of clinical data. In consequence, it also emphasizes the promise of ML driven predictive systems for timely, data driven decision support in the clinical setting, and in so doing, improves patient outcomes.

关键词： Support vector machines Logistic regression Accuracy Nearest neighbor methods prediction algorithms Data models Bayes methods Decision trees Cardiovascular diseases Random forests

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prediction Of Mental Health in Working People Using Machine Learning algorithms

Prediction Of Mental Health in Working People Using Machine ...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Kumar P Senthil Pandi S Jain Bai B Mithun Dineshkumar Department of CSE Rajalakshmi Engineering College Chennai India UST Global Trivandrum India Khoury College of Computer Science Northeastern University Boston United States

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The professional and personal quality of life are both improved by early diagnosis and treatment of mental health issues. Depression is a major factor in disability all over the world. Professionals can treat internal health conditions earlier and improve their patients' quality of life by examining these issues. Depression is the leading cause of mental health problems worldwide. Professionals in the workforce must address early internal health issues to avoid more complex issues. A comprehensive overview of ML and its use in healthcare operations is provided in this paper. In addition, it provides a summary of the author's own research on the application of ML to internal health, highlighting the limitations that are currently in place and the areas that require additional observation. algorithms based on ML are now extremely useful for diagnosing issues and analyzing medical data. Joining information from many sources and AI approaches assisted us with achieving the best exactness of 94.061 with the irregular woodland classifier.

关键词： Accuracy Machine learning algorithms Mental health Medical services Predictive models Depression prediction algorithms Mobile applications Random forests Medical diagnostic imaging

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A Comprehensive Analysis of Machine Learning algorithms for Diabetes prediction: Comparative Performance and Insights

A Comprehensive Analysis of Machine Learning Algorithms for ...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： A. Akilandeswari SAVEETHA SCHOOL OF ENGINEERING SIMATS UNIVERSITY

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Diabetes is one of the most chronic conditions seen worldwide. Its prevalence has been causing many complications, such as cardiovascular diseases, kidney failure, and neuropathy. Early prediction is crucial in helping prevent these from occurring or, at worst, to be affected minimally. In this work, we implemented various machine learning techniques to predict the onset of diabetes based on demographic and health-related data like age, body mass index (BMI), hypertension, heart disease, and blood glucose levels. The used algorithms were XGBoost, Random Forest, Stacked Ensemble, Gradient Boosting Machine (GBM), and Support Vector Machine (SVM). XGBoost could reach a maximum AUC of 0.979653 and a log loss of 0.0789284 with an F1-score of 0.811485. These really demonstrated strong power in the prediction of diabetes. High performance was also highlighted with the Stacked Ensemble Model, reaching an AUe of 0.979579 and an accuracy of 97.22%. These outcomes suggest that the superior ensemble methods, XGBoost and Stacked Ensembles, will provide quality prediction such that earlier diagnosis of patients with diabetes might be achieved consequently and, therefore, good recovery.

关键词： Support vector machines Analytical models Machine learning algorithms Medical services Predictive models Boosting prediction algorithms Market research Diabetes Random forests

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Revolutionizing Athletic Training with Machine Learning: Injury prediction Using Predictive Analytics and Customized Workouts Through Personalization algorithms

Revolutionizing Athletic Training with Machine Learning: Inj...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Tushar Dhar Shukla Shaik Balkhis Banu Malik Bader Alazzam Vijay Kumar Ajmeera Kiran A. Balakumar University Institute of Physical Education & Sports Chandigarh University Mohali India Department of Physiotherapy Fatima College of Health Sciences Al Ain UAE Faculty of Information Technology Jadara University Irbid Jordan Department of Physics Graphic Era Hill University Dehardun India Graphic Era Deemed to be University Dehradun India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Department of ECE K.Ramakrishnan College Of Engineering Trichy India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This research offers the solution to the existing problem by implementing the idea of a unique athletic training based on predictive analytics and personalization algorithms. In particular, we suggest the use of the MLP-LSTM model to forecast athletic injuries and prescribe individualized workout schedules relevant to the specific athlete. Multilayer perceptron and long short-term memory network are used together for designing MLP-LSTM architecture that has the capacity to grasp temporal patterns present in athlete information and predict injuries. It will employ training data, athletes’ profile, and data on their past and present injuries and using statistical and mathematical models to read into it and look for signs that may be precursors to possible injuries. Accordingly, individual training schedules are suggested with the intent to minimize the chances for sustaining an injury while at the same time maximize performance. The creation of this system appears to be fundamentally proactive with regards to athletic training because it puts it in the hands of the coaches and athletes involved to allow prevention of injuries when they are most likely to occur. Based on the experimental results, it can be concluded that MLP-LSTM outperforms most of the traditional machine learning classifiers for the injury prediction. Moreover, the workout routines created by the system have proved to enhance the athletes’ performance and prevent injuries. This research can be useful in changing the existing mode of training of athletes in order to improve on their health and performance in sports.

关键词： Training Schedules Prevention and mitigation Training data Multilayer perceptrons prediction algorithms Predictive analytics Injuries Long short term memory Sports

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Research on Olympic Medal prediction Model Based on Feature Fusion and Dynamic Optimization algorithms

Research on Olympic Medal Prediction Model Based on Feature ...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Yuqi Xiong School of Computer Science and Engineering Southeast University Fuzhou China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper presents an advanced Olympic Medal prediction Model that leverages feature fusion and dynamic optimization algorithms to enhance the accuracy of medal distribution forecasts. The model begins by analyzing the correlation parameters within a feature-normalized framework, focusing on their relationship with historical medal ratios. By employing the principles of dissipation and inclusion of medal ratios, the model determines the optimal weights for various influencing factors. To further refine the model's fit, the data distribution is processed, and a partial pairwise comparison matrix is constructed. The introduction of a distribution parameter S, combined with a genetic algorithm, significantly improves the model's precision. Additionally, a simulated annealing algorithm featuring an adaptive temperature increase strategy is implemented to mitigate the risk of local optima, thereby enhancing the model's overall fitting performance and predictive accuracy. The model is rigorously tested by predicting the medal distribution for the 2028 Summer Olympics in Los Angeles. Comparative analyses with existing models are conducted to validate its effectiveness. The results underscore that the integration of feature fusion and dynamic optimization algorithms markedly enhances the accuracy of Olympic medal predictions. This research provides a robust tool for forecasting Olympic outcomes but also offers valuable insights for the application of advanced algorithms in sports analytics.

关键词： Adaptation models Analytical models Accuracy Heuristic algorithms Simulated annealing Predictive models prediction algorithms Optimization Genetic algorithms Sports

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prediction of Heart Disease Using ML algorithms

Prediction of Heart Disease Using ML Algorithms

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Ashok Kumar Panda Chinmayee Pati Sonali Pradhan Amrutanshu Pradhan Naba Kumar Rath Department Of Computer Science & Engineering Silicon University Bhubaneswar India School of Computer Sciences Odisha University of Technology and Research Bhubaneswar India College of Engineering Bhubaneswar (CEB) Bhubaneswar India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

There are a variety of approaches utilised in the processing of healthcare data. Data mining is utilised quite frequently. The majority of deaths are caused by cardiovascular disorders. It is a risk factor for heart disease. The proportions of risk factors for heart disease can be computed using this technique. Data sources can be categorised based on healthcare factors. Using a data mining classification approach, this system does analysis on such parameters. The datasets are analysed using the Decision Tree Algorithm and the Naive Bayes Algorithm, both of which are written in Python. The Decision Tree Algorithm has the highest precision for heart disease. Introduction

关键词： Heart Maximum likelihood estimation Machine learning algorithms Soft sensors Medical services Machine learning prediction algorithms Classification algorithms Naive Bayes methods Data mining

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Implementation of a Fall prediction System Using Neural Network algorithms on a Microcontroller

Implementation of a Fall Prediction System Using Neural Netw...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Yue-Ru Chuang Yu-His Li Wen-Hui Chen En-Ting Kuo Yu-Chieh Lin Jinn-Yuan Lay Department of Electrical Engineering Fu Jen Catholic University New Taipei City Taiwan Department of Electrical Engineering Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei City Taiwan WUSA Smart Inc. Taipei City Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Based on the needs of hospital rehabilitation centers, this study focuses on posture detection for patients undergoing rehabilitation in hospitals, especially the elderly. The goal is to ensure that nearby medical staff receive alerts as soon as possible when a potential fall is detected, allowing them to intervene before an incident occurs. This paper proposes a fall prediction system that uses sensor fusion and a Feedforward Neural Network (FNN). The system consists of a Bluetooth-enabled microcontroller unit (MCU) integrated with a 6-axis Inertial Measurement Unit (IMU), which is placed on the patient's collar. In this study, the potential movements performed by patients during rehabilitation are classified into nine categories: standing, stomping, stair climbing, stretching, waist twisting, bending over, squatting, walking, and running. Motion data from the IMU is processed using an FNN and k-means clustering for movement classification and abnormal motion detection. If a potential fall is detected, an alarm is sent via Bluetooth to notify nearby medical staff. Finally, the proposed fall prediction system will be implemented on a Bluetooth-enabled MCU module.

关键词： Performance evaluation Microcontrollers Hospitals Sensor fusion Stairs Predictive models prediction algorithms Real-time systems Feedforward neural networks Wearable devices

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prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第1期18卷 372-384页

作者： Wu, Haijie Lin, Weiwei Shen, Wangbo Wang, Xiumin Chen, C. L. Philip Li, Keqin South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Shenzhen 518055 Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.

关键词： Runtime Accuracy Fuzzy logic Training Predictive models Servers Robustness Computer science Artificial neural networks prediction algorithms Deep neuro-fuzzy system edge server heterogeneous device prediction

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