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predictive Modeling of Parkinson's Disease Using Machine Learning Techniques

Predictive Modeling of Parkinson's Disease Using Machine Lea...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Sad Shariar Helali Md. Abdul Based Md. Zubair Rahman Md. Arafat Rahman Dept of Computer Science and Engineering Dhaka International University Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Parkinson's disease (PD) is a common neurodegenerative disorder that affects millions of individuals worldwide. Early and accurate diagnosis is crucial for managing symptoms and improving patient outcomes. this research paper presents a comprehensive approach to predicting Parkinson's disease using the MDVP (Multidimensional Voice Program) dataset by machine learning models integrated with a user-friendly web application. the research uses eight machine learning methods, including Decision Tree, K-Nearest Neighbors, AdaBoost, Logistic Regression, Gradient Boosting, Random Forest, Nearest Centroid, and Naïve Bayes. Among them, Gradient Boosting and Random Forest show the best result with 100.00% accuracy and additionally, the AdaBoost and Decision Tree classifiers also demonstrated high performance, achieving 98.31% accuracy. these findings suggest the potential of these models for distinguishing between Parkinson's disease and healthy individuals. Also, the web application we developed allows users to input voice data and get a prediction about having Parkinson's disease. this tool is designed to be user-friendly, providing quick and reliable results which ultimately lead to better patient care and more efficient use of healthcare resources.

关键词： Accuracy Machine learning algorithms Parkinson's disease Medical services predictive models Nearest neighbor methods Boosting Reliability engineering Decision trees Random forests

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Weather Prediction Platform Using Data Mining

Weather Prediction Platform Using Data Mining

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Computing and Informatics (ICCI), international conference on

作者： Haya K. Elsonbaty Manar A. Ibrahim Noure A. Elnakeib Eman A. Noh Haneen I. Fayez Magy Elbanhawy Ahmed T. Mazrua Ibrahim F. Moawad Omar M Elzeki Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Faculty of Engineering Faculty of Computer Science and Engineering Portsaid University Egypt New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science & Engineering Mansoura University Mansoura Egypt New Mansoura University Egypt Faculty of Computer and Information Faculty of Computer Science and Engineering Sciences Ain shams University Cairo Egypt New Mansoura University Dakhlia Egypt

this study focuses on using the Weka toolkit's machine learning algorithms to forecast weather conditions based on a dataset that includes columns for precipitation, temperature_max, temperature_min, and wind. Forecasting various weather conditions, such as drizzle, rain, sun, snow, and fog, is the goal. A thorough examination has been carried out utilizing a range of Weka algorithms, covering models with higher performance and lesser accuracy. After extensive testing, the best performing logistic algorithms meta. logitboost, meta. Iteration Classifier, ***, trees.j48, and functions. Logisticwere able to predict the weather with exceptional accuracy. these models demonstrated how well they could comprehend and predict weather conditions using the parameters from the given dataset. On the other hand, weather state predictions were relatively less accurate for models using the meta. classificationvia Regression, meta. bagging, ***, misc. input, and *** algorithms. In order to furnish stakeholders with practical and captivating insights, the outcomes were condensed and shown via user-friendly and aesthetically pleasing Google Looker dashboards. With the help of these dashboards, stakeholders may interactively examine and understand the predicted weather conditions that are based on the reliable machine learning algorithms. this research attempts to provide decision-makers with easily accessible, data-driven weather forecasts by combining these predictive algorithms with Google Looker's visualization capabilities. the dashboard system's extensive analysis and visualization are intended to improve the comprehension and use of machine learning in weather prediction, promoting well-informed decision-making across a range of industries dependent on weather-sensitive operations.

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Service Quality Loss of Passenger Car Drive Shafts Based on Rigidity Degradation Law

Service Quality Loss of Passenger Car Drive Shafts Based on ...

引用

Robotics and Computer Vision (ICRCV), international conference on

作者： Junyan Gu Jingang Wang Bixiong Huang Xintian Liu School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

As passenger car drive shafts enter the service phase, considering the impact of material properties, complex working conditions, and driver habits, the trend of change in the key quality characteristic values of the passenger car drive shafts should be nonlinear. Collect data based on the standard method for the wear test of the drive shaft, and fits the wear pattern curve based on the results of the bench wear test of the drive shaft. Furthermore, by analyzing the previous research on the stiffness degradation law of the drive shaft tube, the average service quality loss function of the drive shaft is revised. this provides a theoretical support for the life prediction and reliability analysis of passenger car drive shafts during the service phase.

关键词： Shafts Degradation Accuracy Reliability theory Drives predictive models Rigidity Automobiles Standards Vehicles

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Adapting Speech models for Stock Price Prediction

Adapting Speech Models for Stock Price Prediction

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： Frederic Voigt Jose Alcarez Calero Keshav Dahal Qi Wang Kai von Luck Peer Stelldinger Department of Computer Science University of the West of Scotland / Hamburg University of Applied Sciences Hamburg Germany School of Computing Engineering & Physical Sciences University of the West of Scotland Paisley Scotland Department of Computer Science Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

Large language models (LLMs) have demonstrated remarkable success in the field of natural language processing (NLP). Despite their origins in NLP, these algorithms possess the theoretical capability to process any data type represented in an NLP-like format. In this study, we use stock data to illustrate three methodologies for processing regression data with LLMs, employing tokenization and contextualized embeddings. By leveraging the well-known LLM algorithm Bidirectional Encoder Representations from Transformers (BERT) [1], we apply quantitative stock price prediction methodologies to predict stock prices and stock price movements, showcasing the versatility and potential of LLMs in financial data analysis.

关键词： Adaptation models Machine learning algorithms Large language models Machine learning predictive models Transformers Prediction algorithms Tokenization Encoding Speech processing

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Deep Learning-based predictive Modeling of Building Energy Usage

Deep Learning-based Predictive Modeling of Building Energy U...

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international conference on Energy Conservation and Efficiency (ICECE)

作者： Faizan Hamayat Zeeshan Akram Syed Zubair Electrical Engineering Department University of Engineering and Technology Taxila Taxila Pakistan Artificial Intelligence Lab Sense Digital (Pvt) Ltd Islamabad Pakistan

Buildings are responsible for around 40% of the total global energy consumption and the increase in the energy demand is staggering. therefore, the energy demand prediction has become an essential in order to ensure the stable power supply and to meet the future global energy needs. An accurate power consumption (PC) prediction for buildings using the classical methods is a challenging task. In particular, the dependency of PC modelling on various complex factors and the nonlinearity which exists in buildings systems make an accurate prediction of PC difficult. this research presents an approach for accurately predicting power consumption (PC) in buildings using a black-box model based energy demand prediction algorithm. the algorithm utilizes artificial intelligence and a data-driven approach to address the challenges associated with PC modeling, including complex dependencies and nonlinearity in building systems. Two specific models, a Bi-LSTM based model and a hybrid CNN with Bi-LSTM based model are proposed. Further, the impact of various data normalization techniques on model performance is investigated. Results show an improvement of up to 10 % in PC prediction when compared to existing LSTM based techniques, highlighting the potential for more robust energy consumption modeling and optimization for energy efficiency.

关键词： Energy consumption Power demand Buildings Time series analysis Closed box predictive models Prediction algorithms

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A Machine Learning Approach to Predict the Efficiency of the Semiconductor Neutron Detector

A Machine Learning Approach to Predict the Efficiency of the...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Parveen Malik Manoj kumar Parida Vikas Kumar School of Electronics Engineering KIIT Deemed to be University Bhubeneswar India

ISBN: (纸本)9781665457002

In this research paper, we have developed a novel approach using the machine learning models to predict the efficiency of semiconductor neutron detector for boric acid based converter material. Considering the over head of computational resources and time to compute the efficiency of a neutron detector, it becomes imperative to find the models that can take much lesser resources and time. In this novel work, the model was trained from the earlier research work done on GEANT4 simulation software and the predicted data was also compared with the simulated data. the various machine learning regression model were designed for stacked detector. Further, comparison have been done to find the best fit for the experimental data.

关键词： Computational modeling Detectors Neutrons predictive models Boosting Data models software

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A Comparative Study of Photovoltaic Power Prediction models Based on BP and SVR

A Comparative Study of Photovoltaic Power Prediction Models ...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Feng Lu Zhen Li Xin Wang Lei Zhao Fei Meng Guohua Yang Dispatching & Control Center of State Grid Ningxia Power Co. Ltd Yinchuan Ningxia China School of Electronic and Electrical Engineering Ningxia University Yinchuan Ningxia China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

the power output of photovoltaic (PV) generation is influenced by various environmental factors, exhibiting non-linearity and uncertainty. this paper constructs photovoltaic power prediction models based on BP neural network and support vector regression (SVR) algorithms, and performs a comparative analysis of model performance using metrics such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ). the results show that the BP neural network is suitable for handling complex nonlinear large-scale data, while SVR demonstrates higher accuracy and generalization ability under small sample conditions. this study provides a reference for the selection and optimization of photovoltaic power prediction models.

关键词： Photovoltaic systems Support vector machines Analytical models Accuracy Processor scheduling Computational modeling Neural networks predictive models Computational efficiency Forecasting

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Implementing and Evaluating a P4-based Access Gateway Function on a Tofino Switch

Implementing and Evaluating a P4-based Access Gateway Functi...

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international conference on Advanced Communication Technologies and Networking (CommNet)

作者： El-Mehdi Makhroute Mohammed-Ayoub Elharti Vincent Brouillard Yvon Savaria Tarek Ould-Bachir Department of Computer and Software Engineering Polytechnique Montréal Montreal Canada Department of Electrical Engineering Polytechnique Montréal Montreal Canada

this paper introduces an Access Gate Function (AGF) that has been implemented using the P4 language and evaluates its performance when running on a Tofino switch. through the process of translating specifications from the Broadband Forum (BBF) for Fixed Mobile Convergence in the 5G Core into a P4 code and by conducting data plane simulations on software switch models, we provide evidence that the AGF solution conforms to industry standards and exhibits efficient and scalable performance characteristics. Subsequently, we assess the prototype’s implementation on a Tofino switch, analyzing its capabilities and limitations. the reported results demonstrate that the programmable P4 Tofino switch efficiently handles a significant number of sessions, further reinforcing the potential and practicality of the proposed solution for industrial deployment in fixed-mobile convergence scenarios.

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Next-Gen Breast Cancer Prediction through Adaptive Deep Learning

Next-Gen Breast Cancer Prediction Through Adaptive Deep Lear...

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Mobile Computing and Sustainable Informatics (ICMCSI), international conference on

作者： Pandarinath Potluri Kavyashree Nagarajaiah K.Mohana Lakshmi Manjula Moolbharathi Nagaratnamma Allam Balaram Department of CSE Swarnandhra college of engineering and technology Department of MCA Sri Siddhartha Institute of Technology Tumkur Department of ECE CMR Technical Campus Hyderabad Telangana India govt polytechnic Afzalpur Kalaburgi Karnataka Department of CSE MLR Institute of Technology Hyderabad

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this study enhances the ongoing advancements in breast cancer diagnostic tools by highlighting the importance of employing adaptive deep learning and optimization strategies to achieve high accuracy rates. Advancements in medical technology have facilitated early diagnosis and enhanced treatment outcomes for breast cancer, potentially providing doctors with a valuable tool for early detection that could significantly improve patient outcomes. this study introduces a novel approach, Learning Assisted Breast Cancer Prediction (LABCP), which incorporates an advanced ensemble of Cascaded Inception Model and the Ant Lion Optimization algorithm. the proposed adaptive deep learning scheme harnesses the power of deep neural networks to enhance breast cancer prediction accuracy. the experimental evaluation demonstrates outstanding predictive performance, with an accuracy of 98%. the Cascaded Inception Model effectively captures intricate features within breast cancer images, while the Ant Lion Optimization algorithm optimizes the model parameters, further refining the predictive capability. the combination of these techniques results in a robust and efficient system for early breast cancer detection.

关键词： Deep learning Adaptation models Accuracy Adaptive systems Refining Artificial neural networks predictive models Prediction algorithms Breast cancer Optimization

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Comparative Study of Random Search Hyper-Parameter Tuning for software Effort Estimation 17

Comparative Study of Random Search Hyper-Parameter Tuning fo...

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17th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Villalobos-Arias, Leonardo Quesada-Lopez, Christian Univ Costa Rica San Pedro Costa Rica

ISBN: (纸本)9781450386807

Empirical studies on software effort estimation have employed hyper-parameter tuning algorithms to improve model accuracy and stability. While these tuners can improve model performance, some might be overly complex or costly for the low dimensionality datasets used in SEE. In such cases a method like random search can potentially provide similar benefits as some of the existing tuners, with the advantage of using low amounts of resources and being simple to implement. In this study we evaluate the impact on model accuracy and stability of 12 state-of-the-art hyper-parameter tuning algorithms against random search, on 9 datasets of the PROMISE repository and 4 sub-datasets from the ISBSG R18 dataset. this study covers 2 traditional exhaustive tuners (grid and random searches), 6 bio-inspired algorithms, 2 heuristic tuners, and 3 model-based algorithms. the tuners are used to configure support vector regression, classification and regression trees, and ridge regression models. We aim to determine the techniques and datasets for which certain tuners were 1) more effective than default hyper-parameters, 2) more effective than random search, 3) which models(s) can be considered "the best" for which datasets. the results of this study show that hyper-parameter tuning was effective (increased accuracy and stability) in 862 (51%) of the 1,690 studied scenarios. the 12 state-of-the-art tuners were more effective than random search in 95 (6%) of the 1,560 studied (non-random search) scenarios. Although not effective in every dataset, the combination of flash tuning, logarithm transformation and support vector regression obtained top ranking in accuracy on the highest amount (8 out of 13) of datasets. Hyperband tuned ridge regression with logarithm transformation obtained top ranking in accuracy on the highest amount (10 out of 13) of datasets. We endorse the use of random search as a baseline for comparison for future studies that consider hyper-parameter tuning.

关键词： hyper-parameter tuning software effort estimation machine learning empirical study

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