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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： C. Roopa R.S. Latha M. Dhavamani G. Bommisrilekkhaa P. Angel Shalini R. Dhanyajothi Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu Department of Artificial Intelligence Kongu Engineering College Erode Tamilnadu Department of Mathematics Kongu Engineering College Erode Tamilnadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes.

关键词： Machine learning algorithms Precision medicine Supervised learning Machine learning Predictive models Feature extraction prediction algorithms Medical diagnostic imaging Optimization Diseases

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ESPD-LP: Edge Service Pre-Deployment Based on Location prediction in MEC

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第6期24卷 5551-5568页

作者： Song, Liangjun Sun, Gang Yu, Hongfang Niyato, Dusit Univ Elect Sci & Technol China Key Lab Opt Fiber Sensing & Commun Minist Educ Chengdu 611731 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

The rise of real-time applications, services has made Multi-access Edge Computing (MEC) essential for delivering low-latency, high-performance computing. The effectiveness of MEC, however, is largely contingent on the efficient pre-deployment of services. Despite its importance, efficient service pre-deployment is challenged by the inherent unpredictability of user mobility, the fluctuating conditions of network environments. Accurately predicting user locations, dynamically optimizing resource allocation across geographically distributed MEC servers are complex tasks that are essential to minimizing latency, maximizing data transmission efficiency. The variability in user movement patterns, network bandwidth further exacerbates these challenges, often leading to increased latency, diminished performance, which can negate the advantages offered by MEC. To address these challenges, this paper introduces a novel edge service pre-deployment scheme based on location prediction (ESPD-LP). The ESPD-LP scheme leverages historical user trajectory data to predict future locations, facilitating proactive, strategic resource allocation via a user-centric bidirectional matching algorithm across multiple MEC servers. By pre-deploying services in anticipation of user needs, this approach optimizes data transmission rates, reduces pre-deployment latency, significantly enhancing the overall performance of MEC systems. A comprehensive analysis reveals that the ESPD-LP scheme consistently outperforms similar approaches, with a 41% increase in data transmission rates, a 31% reduction in pre-deployment latency compared to the JO-CDSD, MEC-RDESN schemes, demonstrating consistently superior performance.

关键词： Servers Trajectory Resource management Real-time systems Data communication Base stations Optimization prediction algorithms Performance evaluation Low latency communication Multi-access edge computing service pre- deployment location prediction many-to-many bilateral matching trajectory similarity

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Enhancing the Early Student Dropout prediction Model Through Clustering Analysis of Students' Digital Traces

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IEEE ACCESS 2024年 12卷 159336-159367页

作者： Pecuchova, Janka Drlik, Martin Constantine Philosopher Univ Nitra Fac Nat Sci & Informat Dept Informat Nitra 94901 Slovakia

Educational Data Mining and learning analytics have gained significant prominence in recent years, garnering attention from researchers worldwide. This is primarily due to their potential to improve decision-making processes within higher education. This study utilizes Educational Data Mining to suggest a clustering-based approach for classifying students based on their academic performance, specifically focusing on their interactions within Learning Management Systems in blended learning settings. Log data from the Learning Management System Moodle platform was analyzed to determine if valuable insights could be derived from student activities without the need for personal or invasive data. Given the large dataset, Principal Component Analysis and Uniform Manifold Approximation and Projection were employed for dimensionality reduction. Three unsupervised learning models-BIRCH, DBSCAN, and GMM-were then applied to identify distinct student clusters. The results from the comparison revealed that the BIRCH algorithm was the most effective in accurately categorizing students based on their activities. Further analysis of identified clusters revealed a strong correlation between student engagement and academic outcomes, with two high-risk clusters demonstrating the highest dropout rates. To better understand the reasons behind the found clusters, the course duration was subsequently divided into intervals, and standard machine learning methods were applied to predict at-risk students over time, confirming that early identification of at-risk students using clustering is feasible. This study demonstrates the efficacy of non-invasive data collection and unsupervised learning models in predicting student success, emphasizing the importance of temporal and content-specific analysis. The proposed approach leverages clustering algorithms to enhance the understanding of student behavior by identifying patterns and grouping students with similar learning and engagement profiles

关键词： Clustering algorithms prediction algorithms Machine learning Education Predictive models Data mining Machine learning algorithms Data models Analytical models Standards Educational data mining learning analytics clustering analysis dropout prediction

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Multimodal Deep Learning algorithms for Autism prediction: Integrating Functional MRI and Gut Microbiome Data

Multimodal Deep Learning Algorithms for Autism Prediction: I...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： V. Nandhini K. Ananthajothi Department of Information Technology Misrimal Navajee Munoth Jain Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects an individual's social interactions, communication abilities, and behavioral patterns. Recent studies suggest that both neurological and gastrointestinal factors contribute to ASD etiology, necessitating multimodal diagnostic approaches. However, integrating functional MRI (fMRI) and gut microbiome data remains a critical challenge, especially when datasets lack common individuals. This study explores different Deep Learning methods for multimodal ASD prediction, including Multimodal Variational Autoencoder (MVAE), Generalized Canonical Correlation Analysis (GCCA), and Graph Neural Networks (GNNs). Each algorithm addresses specific challenges: MVAE effectively handles missing modalities, GCCA aligns feature spaces across datasets without requiring paired samples, and GNNs leverage functional connectivity networks to extract graph-based neurobiological patterns. By applying these models to fMRI-derived connectivity matrices and microbiome composition profiles, we systematically evaluate their classification accuracy, robustness, and interpretability. Experimental results demonstrate that GNN-based approaches achieve the highest predictive accuracy (91.62%), outperforming conventional multimodal fusion techniques. MVAE provides strong latent representations but struggles with small, heterogeneous datasets, while GCCA offers a scalable framework for cross-modal correlation learning. This study emphasizes the potential of graph-based deep learning in diagnosing ASD and highlights the significance of integrating multimodal data in research on neurodevelopmental disorders. Future extensions may explore transformer-based architectures and domain adaptation techniques to enhance predictive performance further.

关键词： Deep learning Autism Microorganisms Correlation Accuracy Functional magnetic resonance imaging prediction algorithms Feature extraction Transformers Graph neural networks

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Performance Analysis for Business Insights prediction Using Machine Learning algorithms

Performance Analysis for Business Insights Prediction Using ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Steive James Sudarsan Salaja Silas G. Jaspher W. Kathrine Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Predictive analytics is a crucial tool in business decision-making, enabling companies to forecast trends, optimize strategies, and enhance profitability. This study provides a comprehensive performance analysis of three machine learning algorithms-Random Forest (RF), Artificial Neural Networks (ANN), and Gradient Boosting (GB)-for business insights prediction. A structured dataset undergoes preprocessing, including feature engineering and encoding, before model training. Each algorithm is evaluated based on accuracy, precision, recall, Fl-score, and computational efficiency. Results indicate that Gradient Boosting outperforms the others in accuracy (90.2 %), while ANN offers superior generalization. Random Forest provides a balance between interpretability and performance. This comparative analysis aids businesses in selecting appropriate models based on their computational constraints and prediction goals.

关键词： Radio frequency Machine learning algorithms Accuracy Computational modeling Artificial neural networks Predictive models Boosting prediction algorithms Computational efficiency Business

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Employing Machine Learning algorithms for the prediction of Alzhiemer’s Disease

Employing Machine Learning Algorithms for the Prediction of ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Raja Rajeswari Sethuraman Harshini S Dhaarshieth K R Subhashini M Department of Computer Applications Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Swamy Vivekanandha Medical College Hospital and Research Institute Tiruchengode Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Alzheimer's disease is a neurological condition that progressively worsens over time and impairs thinking, memory, social skills, and behavior. Its onset is most commonly seen in the age groups above 65. The World Health Organization's survey indicates that dementia, including AD, is the sixth largest cause of death globally. For older people, this is the most frequent cause of dementia. The word dementia is used to refer to a wide range of brain disorders that are characterized by behavioural abnormalities, loss of memory and reasoning skills, and difficulty carrying out everyday tasks. In order to lower the death rate, early screening for this condition is essential. Machine learning techniques are quite useful in the identification of the relation between risk factors of Alzheimer’s disease and its incidence. This study examines the relationship between several characteristics that lower the risk of Alzheimer's disease using a benchmarking dataset of 2149 Americans. Early detection of AD and associated symptoms is more successful when classifiers such as Random Forest, Support Vector Machine, K-Nearest Neighbours, XGBoost, CatBoost, and Logistic Regression are used. Classifier performance and AD predictions are evaluated using metrics like as F1-score, Precision, Accuracy, and Recall. According to this suggested study, the Random Forest and CatBoost algorithm predicts the test data with 95.5% accuracy. This score is better than the other modern and latest algorithms.

关键词： Support vector machines Surveys Logistic regression Machine learning algorithms Accuracy Refining prediction algorithms Classification algorithms Alzheimer's disease Random forests

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Conditional Distribution Shift-Domain Adaptive Regression Network for Robotic Milling Force prediction Considering Posture-Dependent Dynamic Properties

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年

作者： Ni, Chang Yang, Jixiang Ding, Han Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

Cutting force prediction is significant for machining vibration reduction, process parameter optimization, and robot performance improvement. Because the dynamic characteristics of a robot are dependent on its poses, many prediction methods applied to machine tools cannot be successfully employed on robot. Thus, this article proposes a conditional distribution shift-domain adaptive regression network (CDS-DARN), which realizes cutting force prediction under different robot postures using acceleration and robot joint angles without conducting modal tests. First, source model parameters are transferred to the target model. Subsequently, the cutting force prediction of the target pose is realized by utilizing the conditional distribution embedding discrepancy loss between the source and target pose unlabeled data, and between the target pose labeled and unlabeled data, as well as the regression loss for the joint domain adaptation. Experimental results of robotic milling with varying process parameters under multiple poses indicate that the CDS-DARN can achieve excellent predictive performance. The root mean square errors of the selected trajectories are reduced by more than 65.0% compared with the network trained directly using the source data and by more than 36.0% compared with the network trained directly using different sets of processing parameters under the target pose.

关键词： Force Robots Feature extraction Milling Dynamics Predictive models Data models Monitoring Service robots prediction algorithms Cutting force prediction dynamic characteristics posture-dependent robotic milling transfer learning (TL)

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Time-Generative Adversarial Networks Enabled Ensemble prediction Method for Energy Consumption of Machine Tools

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第5期21卷 3796-3805页

作者： Dai, Yiqun Xie, Yang Zhang, Chaoyong Liu, Jinfeng Jiangsu Univ Sci & Technol Sch Mech Engn Zhenjiang 212100 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China

The severe energy situation has become a key factor restricting sustainable development, and the contradiction between the processing cost of large-scale computer numerical control (CNC) production and a small number of low-quality experiments urgently needs to be resolved. Therefore, this article proposes a data augmentation-driven ensemble prediction method for the energy consumption of machine tools. First, machining experiments are designed based on a novel mechanism model of energy consumption considering material removal rate. By analyzing the experimental data and fitting the calibration coefficients in the mechanism model, the predictability of the initial cutting energy consumption model is demonstrated. Then, a time-series generative adversarial network is presented to extract the features of the entire operating process and enhance power samples. Meanwhile, extreme gradient boosting (XGBoost) is trained based on enhanced samples, and time series prediction is performed on the total process of machine tools. To verify the effectiveness of the generated data, the effects of various data augmentation methods on energy consumption prediction are compared. The experimental findings demonstrate that TG-XGBoost can better cover the original data distribution and generate high-quality samples, thereby effectively characterizing the cutting power model and predicting the error between cutting and overall energy consumption, ultimately improving the accuracy of energy efficiency prediction.

关键词： Energy consumption Predictive models Machine tools Data models Training Machining Data augmentation Analytical models prediction algorithms Feature extraction energy consumption ensemble learning machine tool

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Hybrid Classical-Quantum Generative algorithms for Financial Modelling and prediction

Hybrid Classical-Quantum Generative Algorithms for Financial...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Santanu Ganguly Xing Liang Dimitrios Makris SandboxAQ Surrey UK Kingston University London Surrey UK

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we explore emerging research areas in the application of hybrid classical quantum machine learning (QML) in finance. We focus on hybrid classical-quantum models that have gained significant interest in the financial world for various applications. We utilise a real-world cryptocurrency dataset to train a quantum generative adversarial networks (qGAN), and implement the real-life NVIDIA stock price data on quantum LSTM (QLSTM), comparing its performance with classical Long Short Term Memory (LSTM) model. Additionally, we investigate the application of quantum circuit Born machine (QCBM) using a toy dataset for applications in finance. For the qGAN, we define quantum circuits for generators and showcase the potential for future quantum advantage in finance through QML, possibly in combination with QLSTM and QCBM. Our findings suggest that, while training classical data is more efficient on LSTM, the test predictions of QLSTM is slightly superior to its classical counterpart making it a promising candidate for financial forecasting and analysis for timeseries type data.

关键词： Training Toy manufacturing industry Time series analysis Finance Machine learning prediction algorithms Generative adversarial networks Data models Long short term memory Financial industry

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Evaluating the Effectiveness of ML algorithms in Water Potability prediction Aligned with SDGs

Evaluating the Effectiveness of ML Algorithms in Water Potab...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Saef Ullah Miah Abhijit Bhowmik Tahmid Alam Sifat Rahman Ahona Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This research addresses the critical need for accurate water quality prediction, aligning with Clean Water and Sanitation (SDG 6) and Good Health and Well-being (SDG 3). Utilizing a diverse dataset of water quality attributes, we developed and evaluated multiple machine learning models to predict water potability, contributing to SDG 9 (Industry, Innovation, and Infrastructure) through technological advancement. Our primary contribution is a Hyperparameter-Tuned Histogram Gradient Boosting Classifier, which outperformed several other algorithms, achieving an accuracy of 67%, a ROC-AUC score of 70%, and an F1-score of 66%. The study highlights challenges of class imbalance and feature distribution in water quality prediction, addressing aspects of SDG 11 (Sustainable Cities and Communities) by improving urban water management. Our findings have significant implications for water resource management, supporting SDG 12 (Responsible Consumption and Production) through efficient resource utilization. While acknowledging limitations such as single dataset reliance, this research combines modern machine learning with comprehensive analysis to enhance water quality assessment. It contributes to SDG 17 (Partnerships for the Goals) by providing a framework for collaboration between technology and environmental sectors. Future directions emphasize improving model performance and real-world applicability, potentially supporting SDG 13 (Climate Action) by enhancing resilience to water-related climate impacts.

关键词： Histograms Accuracy Signal processing algorithms Water quality Predictive models prediction algorithms Boosting Classification algorithms Water resources Meteorology

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