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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with Engineering applications

computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Parallel solution and optimization of microphysics module in GRAPES physics process 2

Parallel solution and optimization of microphysics module in...

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2nd International Conference on Big Data, Computational Intelligence, and applications, BDCIA 2024

作者： Xu, Aokang Jia, Jinfang School of Computer Technology and Applications Qinghai University Qinghai Xining810016 China Intelligent Computing and Application Laboratory of Qinghai Province Qinghai Xining810016 China

ISBN: (纸本)9781510689053

Global and Regional Assimilation and Prediction System (GRAPES) is a domestically developed numerical weather prediction system in China. The microphysical process is the key physical process of cloud formation and precipitation and is also one of the hot spots in the calculation of the whole model. Despite the parallel processing capabilities of MPI and OpenMP, the computation time of the microphysics process remains relatively long. In order to achieve finer-grained parallelization, the code is ported to Graphics Processing Unit (GPU) using a combination of memory coalescing and array scalarization methods. In addition, to address the issue of underutilization of GPU resources when multiple processes share a single GPU, the Compute Unified Device Architecture (CUDA) Multi-Processing Service (MPS) is introduced in the microphysics module to improve GPU utilization. Experimental results show that for a resolution of 50km, excluding data transfer time, the speedup achieved by using 4 GPUs in conjunction with 36 CPU cores reaches 11.59, and the speedup excluding data transfer time is 18.59. It has been verified that the parallel computation of the microphysics process on GPUs is stable and correct. © 2025 SPIE.

关键词： Graphics processing unit

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A Comparative Analysis of User Experience and Educational Outcomes in Math Memory Games

A Comparative Analysis of User Experience and Educational Ou...

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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Martin, Nicholas Nathanael, Raffel Jeriyanto, Doni Hermawan, Eric Savero Fung, Janshen Panggabean, Francisco Maruli School of Computer Science Bina Nusantara University Game Application & Technology Program Computer Science Department Jakarta Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350368802

This research explores multiple varieties of effective and child-friendly learning approaches for mathematics and memorization among young children in today's increasingly digital age considering their specific developmental needs. This type of approach includes in-person teaching, engaging online videos, interactive textbooks, and innovative educational games. This research employs a detailed comparative analysis, considering both 'User Experience' and measurable learning outcome factors, and spans a range of age groups from preschoolers to elementary school children. Through highlighting the strengths and weaknesses of each method, this research aims to inspire the adoption of efficient, child-centric, and novel teaching techniques by educators and institutions, thereby advancing the quality of education in Indonesia. The results show that using serious game in education presents high usefulness score alongside positive user experience, suggesting its viability for educational purposes. © 2024 IEEE.

关键词： Contrastive Learning

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring 5

DL-Based Somnolence Detection for Improved Driver Safety and...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Neeraja, Peddinti. Kumar, R.G. Kumar, M. Sunil Liyakat, Kazi Kutubuddin Sayyad Vani, M. Sowmya School of Computing Department of Computer Application AP Tirupathi India Siddharth Institute of Engineering & Technology Computer Science and Engineering AP Puttur India School of Computing Department of Computer Science and Engineering AP Tirupathi India Brahmdevdada Mane Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India

ISBN: (纸本)9798350383522

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads. © 2024 IEEE.

关键词： Convolution neural network deep learning Drowsiness detection transfer learning

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Heart Disease Predictive Analysis Using Machine Learning Approaches 6

Heart Disease Predictive Analysis Using Machine Learning App...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rana, Anamika Malik, Sushma Chauhan, Madhu Kumar, Prashant Maharaja Surajmal Institute Department of Computer Application New Delhi India Institute of Innovation in Technology & Management Department of Computer Application New Delhi India Jagannath International Management School New Delhi India

ISBN: (纸本)9798350304480

Machine Learning (ML) has found widespread applications in the healthcare sector worldwide, including the diagnosis and treatment of heart diseases, locomotor disorders, and various other medical conditions. ML methods have revolutionized healthcare by enabling the analysis of large and complex medical datasets, leading to valuable insights and predictions that aid healthcare professionals in providing better patient care. ML's ability to analyze vast amounts of healthcare data, uncover patterns, and make predictions has significant potential to improve patient outcomes, optimize medical workflows, and advance medical research. However, it's essential to address privacy and ethical considerations when using ML in healthcare, ensuring the responsible and secure use of sensitive patient information. Supervised Learning methods like SVM, Random Forest, and Logistic Regression are used for the analysis of the dataset downloaded from Kaggle. The various performance parameters such as Precision, F-1 score, Accuracy, and Recall were used to compare the performance of different ML classification techniques. Among the various methods evaluated, the Random Forest classification algorithm was found to outperform the other methods across the fourteen available parameters. © 2023 IEEE.

关键词： Heart Disease Logistic Regression Machine Learning (ML) Random Forest SVM

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Research on Environmental Perception Based on YOLOv8 and LiDAR Data Fusion 4

Research on Environmental Perception Based on YOLOv8 and LiD...

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4th IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Luo, ZhenHong Li, HuiLing Li, Ting Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350363586

To enhance the sensing capability of driverless cars in complex dynamic scenarios, a fusion method of radar and depth camera is emphasized and a technical framework for integrating multi-source sensors is proposed. A YOLOv8 neural network detection model combined with a self-attention network structure is first used to achieve correct recognition and depth estimation of vehicles and pedestrians, then further distance data of dynamic environment is acquired by M10P LiDAR, and finally information from D435i camera and radar information are fused by matrix projection. In the experiments, the conventional YOLOv8 model was compared to the YOLOv8 model enhanced with self-attention, the camera and LIDAR measurements were evaluated against the fusion results. The experimental results demonstrate that the new model significantly improves the confidence and stability of target detection, while the fused distance measurements are more accurate than those from individual sensors. © 2024 IEEE.

关键词： Sensor data fusion

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Multi-scale Feature Fusion for Enhanced Person Re-identification 3rd

Multi-scale Feature Fusion for Enhanced Person Re-identific...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wang, XiaoHui Sun, Yulin Zou, Zhipeng Liang, Xiaoyang Sun, Zhenyu Liu, Wei Shandong Business Institute Yantai China Shandong Xuewen Information Technology Co. Ltd. Jining China Technology of Computer Application Yantai Engineering and Technology College Yantai China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9789819624676

In the realm of surveillance and security, person re-identification (Re-ID) stands as a critical challenge, particularly across non-overlapping camera networks. The essence of this challenge lies in the accurate and efficient identification of individuals by leveraging features captured from multiple perspectives. Addressing this, we introduce the Contextual Multi-Scale Network (Asy-CMSNet), a novel framework designed to enhance the Re-ID process through the integration of asymmetrical convolution and an innovative loss function, the Enhanced TriHard (E-TriHard). Unlike traditional approaches that rely on the stacking of convolutional layers, Asy-CMSNet employs a sophisticated mechanism that allows for the nuanced extraction and integration of multi-scale features, thereby optimizing the identification process across diverse pedestrian appearances. Central to our framework is the notion that effective person Re-ID necessitates the consideration of both global and localized pedestrian details, ranging from broad clothing characteristics to subtle accessories, which our model adeptly captures and utilizes. Through extensive experimentation across several benchmark datasets, Asy-CMSNet demonstrates superior performance in person Re-ID challenges, highlighting a substantial progression in the area. Our findings not only set new performance benchmarks but also pave the way for future research into more efficient, scalable, and robust person Re-ID systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking

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A Survey of Privacy Preserving Methods based on Differential Privacy for Medical Data 7

A Survey of Privacy Preserving Methods based on Differential...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Yan, Haicao Yin, Menghan Yan, Chaokun Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application Kaifeng China

ISBN: (纸本)9798350372328

Using deep learning and federated learning in the medical domain has facilitated convenience in diagnosing and treating patients. However, the current privacy leakage problem has made patients more cautious about personal privacy security. Privacy security has become an urgent issue. Differential privacy is a cryptography technique that makes it impossible for attackers to identify personal data by adding noise to the training process. In this work, we first provide a complete overview of differential privacy technologies. Then, we will review existing differential privacy works in the medical field. Finally, we analyze the existing problems of differential privacy and propose possible avenues for further research. © 2024 IEEE.

关键词： Deep learning

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Machine Learning to Predict Cardiovascular Disease: Systematic Meta-Analysis 6

Machine Learning to Predict Cardiovascular Disease: Systemat...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Shuja, Mirza Qtaishat, Ahmed Mishra, Hari Mohan Kumar, Mukesh Ahmed, Bilal Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

People do not have much time to think about their health in today's fast-paced society because of how busy their lives are. The overscheduling of people's lives and a widespread disregard for their health are two factors that contribute to the rise in the incidence of disease. Additionally, the great majority of people have disability that prevents them from operating properly, such as cardiovascular illness. These people are unable to operate normally because of their disabilities. According to the statistics provided by the World Health organization (WHO), cardiovascular disease is responsible for more than one-third of all deaths. Because of this, it is very necessary for anybody working in the medical industry to be able to evaluate the chance that a patient may develop cardiovascular disease. However, because hospitals and the healthcare industry produce such a vast amount of data, it may be difficult to do research owing to the sheer amount of information that is available. The use of methods that are based on Machine Learning (ML) by medical professionals has the potential to cut down on the amount of time and effort spent formulating predictions and organising data. Because of this, we have been talking about the factors that lead to heart disease as well as the methods that are involved in machine learning. We evaluated and compared the efficacy of a wide variety of well-known ML algorithms that were utilised in the experiment, and we made inferences about cardiovascular disease using these techniques. In the end, the purpose of this research is to determine whether a ML system is capable of reliably predicting cardiac illness.. © 2023 IEEE.

关键词： Forecasting

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Future Directions of Artificial Intelligence and Machine Learning in Healthcare: A Systematic Analysis and Mapping Study 6

Future Directions of Artificial Intelligence and Machine Lea...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Mishra, Hari Mohan Ahmed, Bilal Shuja, Mirza Qtaishat, Ahmed Kumar, Mukesh Lovely Professional University School of Computer Application Phagwara India Sohar University Department of Information Technology Sohar Oman

ISBN: (纸本)9798350346961

In order to organise previously conducted research on using machine learning in medical contexts, the authors of this paper employ a method known as systematic mapping. In order to do this, we looked through the abstracts of a variety of publications, including medical journals, healthcare periodicals, and conference proceedings, looking for the phrase 'use of artificial intelligence and machine learning in healthcare.' After doing a search on Google Scholar, which resulted in the retrieval of 500 papers, we classified these studies in accordance with their objectives, approaches, primary concerns, and illnesses. With the use of this technique, we were able to organise our findings into the five categories that are as follows: privacy and security;a framework for privacy and security;interpretable machine learning;medical image assessment;electronic health record processing;and transfer learning. In addition, we found that the evaluation of medical images is the topic that receives the most research, that interpretable machine learning and explainable artificial intelligence are becoming increasingly popular, and that most authors are primarily interested in cancer research. To restate our mission, we want to provide future generations of scholars with an accurate picture of where the field is now and where it is headed.. © 2023 IEEE.

关键词： Machine learning

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