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检索条件"任意字段=5th International Conference on Fun with Algorithms"

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Predictive Modeling of Cardiovascular Disease Using Machine Learning: A Comparative Analysis 5

Predictive Modeling of Cardiovascular Disease Using Machine ...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Tonni, Sadia Islam Alam, Md Ashraful Sohel, Amir Khan, Chonchal Onik, Md.Khaledur Rahman Pranto, Ismail Hossain Jahangirnagar University Institute of Information Technology Dhaka Bangladesh Colorado State University Department of Computer Science CO United States Project Management St. Francis College Department of Information Technology New York United States Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University- Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530983

Cardiovascular disease remains one of the leading causes of death worldwide, necessitating the development of efficient diagnostic tools. this study presents a machine learning framework for predicting cardiovascular disease based on clinical and demographic data. We employ a comprehensive data preprocessing pipeline that includes handling missing values, normalizing data, and balancing the dataset to ensure robust model performance. Feature extraction and selection techniques are applied to identify the most relevant predictors of cardiovascular risk, optimizing model performance and reducing computational complexity. Eight machine learning algorithms were employed to predict cardiovascular disease outcomes, including logistic regression, decision trees, random forest, support vector machines, k-nearest neighbors, naïve Bayes, XGBoost, and AdaBoost model. Our approach achieved an accuracy of surpassing 98%, demonstrating the potential of ML techniques in aiding early diagnosis and improving patient outcomes. this comparative analysis highlights the strengths and limitations of each algorithm, providing insights into the most suitable models for clinical use. © 2025 IEEE.

关键词： Support vector machines

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AI in the Detection of Breast Cancer and Related Ethical and Legal Issues 5th

AI in the Detection of Breast Cancer and Related Ethical and...

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5th international Ethical Hacking conference, eHaCON 2024

作者： De Mukhopadhyay, Keya Guha, Tapas Basu, Abhisikta Department of Biotechnology Institute of Engineering and Management University of Engineering and Management Kolkata India Department of Computer Science Engineering Techno International New Town India University of Engineering and Management Kolkata India

ISBN: (纸本)9789819784561

Breast Cancer accounts for a significant percentage of malignancy among women globally. It is a disorder where abnormal breast cells develop uncontrollably and form tumours when left untreated, the tumour could spread throughout the body and become fatal. So, early detection is critical for survival. the clinical outcomes of breast cancer can be significantly improved with an accurate and timely diagnosis. Significant advancements in breast cancer diagnosis and customized treatments have been made possible, by the emergence of Artificial Intelligence particularly in the field of image analysis, the K-Nearest Neighbors algorithm is one of the simplest and easily interpretable supervised ML algorithms, which can determine whether the cancer lumps are malignant or benign. KNN, SVM, and GRNN are machine learning algorithms that make use of an openly accessible dataset, features taken from images, data pre-processing techniques, and Principal Component Analysis (PCA) in order to improve accuracy and decrease diagnostic time. Artificial Intelligence can identify both missed cancers and breast tissue features, which in turn, can help to predict future cancer development. Some Artificial Intelligence algorithms excel at predicting patients with a high risk of interval cancer, which is aggressive. When no cancer was clinically detected by screening mammography, even Artificial Intelligence algorithms trained for short time horizons can forecast the chance of developing cancer for up to five years in the future. the WHO reports that low- and middle-income nations account for over 80% of fatalities from breast and cervical cancer. In the developing economic scenario of India, one in 28 women is likely to develop breast cancer. the Artificial Intelligence detection model is a cost-effective precautionary measure for the early detection of breast cancer in women. the data protection laws should be convenient enough to carry out the test with the informed consent of the patients

关键词： Diseases

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5th international Symposium on Dynamic Response and Failure of Composite Materials, DRAF 2024

5th International Symposium on Dynamic Response and Failure ...

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5th international Symposium on Dynamic Response and Failure of Composite Materials, DRAF 2024

ISBN: (纸本)9783031776960

the proceedings contain 38 papers. the special focus in this conference is on Dynamic Response and Failure of Composite Materials. the topics include: Detection of Impact Damages on Full Scale Wing Using Distributed Fiber Optics Sensors Network;evolution of Unstable Skin-Stringer Debonding Propagation in Composite Aircraft Structures: Implications on Damage Tolerant Design;a Benchmark Between Conventional and Custom Heat Treaments for Inconel 718 Alloy Processed through Cold Metal Transfer Technology;real-Time Shearography Measurements of 3D Printed Auxetic Specimens During Flexural Tests;analysis of the Crushing Behavior of Flat Composite Plates Produced by Sheet Molding Compound;effect of Tightening Torque on Bearing Performance of Kevlar Fiber Composite Bolted Joints Produced by Additive Manufacturing;general Damage Detections on Composite Panels Using Computer Vision algorithms;Crashworthiness of C-Shaped CFRP Composites: A Numerical and Experimental Study;a Numerical-Experimental Study on the Impact Behaviour of Additive Manufactured bcc Lattice Structures;development of a Numerical Methodology Able to Simulate the Unstable Mixed Mode Delamination Growth in Stiffened Composite Panels Under Cyclic Loading Conditions;Numerical Investigation on the Static and Dynamic Behaviour of an Additive Manufactured UAV’s Horizontal Tail;Low Velocity Impact (LVI) Simulation of a CFRP Ply-Drop Laminates: A Numerical Analysis;structural Sandwich Panels for Roofing Systems in Civil Applications;Comparison of Various Processing Treatments on Uniaxial Compression Performance of Large Scale Additively Manufactured thermoplastic ABS Chopped Fiber Composite Structures;layer Height Treatments on Uniaxial Compression Performance of Industrial Large-Scale Additively Manufactured Polymer Composite Structures;homogenization Techniques for Nanocomposites: A Comprehensive Review.

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Innovative Tunes: Utilizing Machine Learning for Predicting Spotify Music Popularity Based on Audio Features 5th

Innovative Tunes: Utilizing Machine Learning for Predicting ...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Joshi, Shubham Gupta, Neha Mahajan, Rupali School of CSE Symbiosis International University Pune India School of CSIT Symbiosis University of Applied Sciences Indore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9783031770746

the digitalization of music has brought significant changes to popular music in the age of streaming media. As the number of users of a platform like Spotify continues to grow, it has become one of the most important online music providers today.. People rely on the Spotify app to listen to their favorite artists and discover new music. this study aims to investigate the relationship between music data, specifically audio features (such as key and tempo) extracted from the Spotify database, and popular music (measured by the number of streams a song receives on the Spotify platform). the main goal is to create an accurate and reliable model that can accurately predict whether a song will become a hit or not. To achieve this goal, researchers looked at four different machine learning algorithms: linear regression, random forest classifiers, and K-means clustering. these algorithms are used to analyze data and find patterns that help predict popular songs accurately. Finally, the research proposed a predictive model that uses machine learning techniques to determine the importance of music. Using these standards, songs can be classified according to their needs, thus providing a better perspective on artists, music producers, and the music industry as a whole. It is hoped that this research will help to better understand the factors that influence the success of the song and pave the way for greater decision-making in music. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Decision making

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UAV countermeasures: status quo, breakthroughs, challenges and future prospects 5

UAV countermeasures: status quo, breakthroughs, challenges a...

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5th international conference on Physics and Engineering Mathematics, ICPEM 2024

作者： Wei, Zairan Liu, Weiwei General Education College Shandong Huayu University of Technology 968 University East Road Shandong Dezhou City China

ISBN: (数字)9781510689169

ISBN: (纸本)9781510689152

In recent years, Unmanned Aerial Vehicles (hereinafter referred to as UAVs) have been extensively utilized, however, the security risks associated with them are prominent and pose a serious threat to critical areas. Consequently, there is an urgent need for research and development of UAV countermeasures. this paper provides a comprehensive analysis of countermeasures against UAVs, encompassing the classification and principles of signal interference, physical countermeasures, and laser-based countermeasures. Regarding key technological advancements, phased array radar and opto-electronic monitoring technology have undergone significant innovations in monitoring and identification capabilities. the former employs multi-unit coordination to accurately detect various types of UAVs while the latter combines multi-spectral imaging with advanced algorithms for precise identification purposes. the intelligent countermeasure decision system utilizes machine learning to construct an evaluation system that makes intelligent choices and dynamically optimizes responses based on threat levels and environmental factors. the development of countermeasure equipment focuses on miniaturization, integration, optimization of interference equipment through new components as well as enhancing reliability through multifunctional system integration components. Despite achievements obtained through laboratory simulation and field testing evaluations systems still face challenges such as complex electromagnetic environments, legal ethics considerations, and cost-effectiveness concerns. therefore it is imperative to integrate a comprehensive multi-technology research and development framework while strengthening international cooperation efforts along with standard setting initiatives to ensure that safety standards are met while promoting further advancements in the field of UAV countermeasures. © 2025 SPIE.

关键词： international cooperation

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Short-Term Wind Speed Forecasting Using Different LSTM Deep Learning Methods 5th

Short-Term Wind Speed Forecasting Using Different LSTM Deep ...

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5th international conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Dev, Upama Ahmad, Shameem Uddin, Mohammad Rabib Dhar, Ratnajit Mubarak, Hamza Hazari, Md. Rifat Mekhilef, Saad Seyedmahmoudian, Mehdi Stojcevski, Alex Alshammari, Obaid Department of Electrical and Electronic Engineering American International University-Bangladesh Dhaka1229 Bangladesh Department of EEE Chittagong University of Engineering and Technology Chattogram4349 Bangladesh School of Engineering and Built Environment Griffith University SouthportQLD4222 Australia School of Science Computer and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia Department of Electrical Engineering College of Engineering University of Hail Hail2240 Saudi Arabia

ISBN: (纸本)9789819791118

Accurate wind energy forecasting is crucial for Bangladesh as it seeks to diversify its energy mix and reduce dependence on fossil fuels. Moreover, precise predictions can optimize wind farm operations, enhancing grid stability and economic viability in the country's emerging renewable energy sector. this study explores wind energy potentiality for Inani Beach, Bangladesh, by forecasting wind speed using various deep learning models. To evaluate their performance based on important criteria, models including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)-LSTM, and CNN-BiLSTM (Bidirectional LSTM) were used. Input parameters included time, barometric pressure, height, and humidity for 2014–2015. the CNN-BiLSTM model was specifically proposed for wind speed potential forecasting and compared against other DL models. Results indicated the superior performance of CNN-BiLSTM compared to other algorithms, with CNN-LSTM showing the weakest performance. CNN-BiLSTM achieved the best results, boasting Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 0.36 and 0.52, respectively. In contrast, CNN-LSTM underperformed with MAE and RMSE values of 0.42 and 0.6, respectively. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind forecasting

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GNSS Time Synchronization Attack Detection Algorithm Based on Clock Bias Change Covariance

GNSS Time Synchronization Attack Detection Algorithm Based o...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Peng Jia Qingmin Liao Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

this paper proposes a GNSS Time Synchronization Attack (TSA) detection algorithm based on clock bias change covariance. By leveraging the synchronization characteristics in the variation patterns of TSA signals, the algorithm effectively detects TSA by calculating the covariance of clock bias changes. Experimental results demonstrate that, compared to the clock drift correlation algorithm, this algorithm not only achieves higher detection sensitivity and accuracy, enabling a clear distinction between normal and TSA signals, but also operates faster, detecting TSA 17.86% earlier on average. Additionally, it reduces computational complexity, saving 17.86% of processing time. this practical detection algorithm is particularly suitable for resource-constrained hardware devices, enhancing the GNSS system's resistance to TSA, ensuring timing accuracy and system stability, and providing reliable security for critical infrastructure.

关键词： Global navigation satellite system Machine learning algorithms Accuracy Sensitivity Heuristic algorithms Hardware Critical infrastructure Synchronization Detection algorithms Clocks

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A Novel Roof Detection Algorithm Based SLSC Supervoxel for Airborne LiDAR Point Cloud Data

A Novel Roof Detection Algorithm Based SLSC Supervoxel for A...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Lili Yue Mengqing Yun Di Han School of Marxism Jilin Communications Polytechnic Changchun China Cloud Computing Center China Telecom Chengdu China International Education Jilin Communications Polytechnic Changchun China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

A new roof detection framework is proposed in this article for detecting roofs from raw point cloud obtained from ALS (Airborne Laser Scanner). there are two main problems solved by the framework, the first problem is that the traditional supervoxel-based algorithm cannot provide accurate feature extraction regions, leading to degradation of classification performance. the second problem is that traditional individual roof segmentation algorithms suffer from the difficulty of ensuring a balance between efficiency and accuracy. To address the first problem, a SLSC (Supervoxel-based Linear Spectral Clustering Segmentation) algorithm is proposed for the feature extraction, which takes into account the correlation of local neighbor changes in terms of supervoxels and provides suitable neighbors for feature computation. Experiments are conducted on two ALS point cloud datasets to evaluate the performance of the algorithms in this article. this algorithm achieved advanced results on two datasets, with overall accuracies of 73.29% and 81.21%, respectively.

关键词： Point cloud compression Accuracy Buildings Lasers Clustering algorithms Feature extraction Power electronics Classification algorithms Detection algorithms Random forests

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Interconnected through Robotic System in English Foreign Language Education 5th

Interconnected Through Robotic System in English Foreign Lan...

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5th international conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Muthmainnah, M. de Barros Cardoso, Luís Miguel Oliveira Kaur, Dalwinder Burhamzah, Muftihaturrahmah Aeni, Nur Al Yakin, Ahmad Sharma, Aditi Teacher Training and Education Faculty Indonesian Language Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Portugal Department of Languages and Communication Sciences Polytechnic Institute of Portalegre Portalegre Portugal Manipal Globalnxt University Kuala Lumpur Malaysia English Study Program Universitas Negeri Makassar Makassar Indonesia Teacher Training and Education Faculty Civic Education Study Program Universitas Al Asyariah Mandar Polewali Mandar Indonesia Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

ISBN: (纸本)9789819761050

Robots have become increasingly important in a variety of life domains as a result of the rapid advancement of technology. Additionally, robots are employed in education as teaching assistants in science, art, and language courses. this research aims to explore the potential of robotic systems as a multipurpose tool for increasing cultural awareness, language proficiency, and communicative competence in the GenAI era. Language games, conversation simulations, and real-time feedback mechanisms are just a few examples of interactive scenarios enabled by virtual robot systems or tutor robots that enable immersive language experiences that go beyond the limitations of conventional schooling. this research contributes knowledge that explores how AI can be used to personalize language teaching according to the needs and learning styles of each student in higher education. the total population of undergraduate students involved in this research was 350 for the 2023 academic year, with 24 students selected purposefully based on sample characteristics from this group who were exposed to using AI for one semester. the research method used is a mix method with quantitative analysis of the results of surveys and interviews with students who are known to have given positive responses and proposes a comprehensive strategy for using AI robot technology in EFL classrooms, which can significantly improve effective learning outcomes. the survey results also show that if educators have a solid understanding of the relationship between linguistic acquisition and technological advances, they will be better prepared to design engaging, individualized lessons that prepare their students to use the language effectively in real-world contexts. the robot-assisted language learning (RALL) approach used in this study, with the integration of robots in the classroom, has the potential to change the way people learn languages to make it more interesting, fun, and motivating, increase engagement

关键词： Students

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A Comparative Study of Intelligents algorithms for Optimizing Lithium-ion Battery thermal Management Systems

A Comparative Study of Intelligents Algorithms for Optimizin...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Hajar El Yassini Kenza Oufaska Khalid El Yassini Rachid Bannari Engineering Sciences Laboratory National School of Applied Sciences Ibn Tofail University Kenitra Morocco College of Engineering and Architecture LERMA Lab International University of Rabat Sala Al Jadida Morocco IA Laboratory Faculty of Sciences Moulay Ismail University Meknes Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Controlling the temperature of lithium-ion batteries, also known as BTMS (Battery thermal Management System), has become a crucial factor in guaranteeing their smooth operation, safety and lifespan, particularly in demanding fields such as electric vehicles and energy storage devices. To meet the thermal challenges posed by these batteries, several avenues have been explored. On the one hand, there are classic methods based on thermal modelling and analytical optimization, and on the other, more modern approaches involving intelligent algorithms. the smart algorithms, often supported by artificial intelligence and advanced optimization techniques, open up new prospects for improving the efficiency and precision of thermal control systems. It is interesting to note that in 2023, research work in this field saw a sharp increase, with growing interest in solutions incorporating AI. this study proposes a comparative analysis of these intelligent approaches applied to BTMS, based on data from the Web of Science database.

关键词： Lithium-ion batteries Machine learning algorithms Temperature thermal engineering Machine learning thermal management thermal analysis Temperature control Safety Optimization

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