检索结果-内蒙古大学图书馆

PlayerRank: Leveraging learning-to-Rank AI for Player Positioning in Cricket

IEEE ACCESS 2024年 12卷 177504-177519页

作者： Hassan, Bilal Clough, Clare Siddiqi, Yusra Faizan Ali, Rao Arshed, Muhammad Asad London Metropolitan Univ Sch Comp & Digital Media London N7 8DB England Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England METICS Solut Ltd London IG3 9JA England Univ Kent Sch Comp Canterbury CT2 7NZ Kent England Univ Management & Technol Dept Software Engn Lahore 54770 Pakistan

Player prioritization is crucial in sports analysis, yet prioritizing based on playing position is underexplored. This paper focuses on using learning-to-rank machine learning models to select the best players for slots within a cricket team's batting order in Twenty20 International (T20I) matches. The aim is to build and train position-specific models to rank potential players for each position in the batting order. These models will use listwise ranking algorithms and an artificial neural network architecture to provide data-driven player rankings, enhancing impartiality and performance focus. Each position-specific model is trained to rank players based on their suitability for that position in the batting order, considering factors like performance metrics and specialization. The models are designed to increase impartiality and focus on player performance. The models achieve an average ordered pair accuracy of over 94%, demonstrating their effectiveness in ranking players for specific batting positions. The specialization of positions enhances the utility of the recommendations, providing a more informed approach to player selection. This study highlights the value of using machine learning models to prioritize players based on their suitability for specific batting positions in T20I matches. The models offer an im-partial and performance-focused approach, enhancing the overall quality of player selection in cricket teams.

关键词： Sports machine learning Accuracy Prediction algorithms machine learning algorithms Deep learning Measurement Random forests Performance analysis Data models Cricket player prediction deep learning prediction ranking algorithms learning to rank

来源：评论

学校读者我要写书评

暂无评论

AI Alone isn't Ready for Chip Design: A Combination of Classical Search and machine learning May be the Way Forward

引用

IEEE SPECTRUM 2024年第12期61卷 38-43页

作者： Majumdar, Somdeb Mallappa, Uday Mostafa, Hesham

CHIP DESIGN has come a long way since 1971, when Federico Faggin finished sketching the first commercial microprocessor, the Intel 4004, using little more than a straight-edge and colored pencils. Today's designers have a plethora of software tools at their disposal to plan and test new integrated circuits. But as chips have grown staggeringly complex-with some comprising hundreds of billions of transistors-so have the problems designers must solve. And those tools aren't always up to the task. square Modern chip engineering is an iterative process of nine stages, from system specification to packaging. Each stage has several substages, and each of those can take weeks to months, depending on the size of the problem and its constraints. Many design problems have only a handful of viable solutions out of 10(100) to 10(1000) possibilities-a needle-in-a-hay-stack scenario if ever there was one. Automation tools in use today often fail to solve real-world problems at this scale, which means that humans must step in, making the process more laborious and time-consuming than chipmakers would like. square Not surprisingly, there is a growing interest in using machine learning to speed up chip design. However, as our team at the Intel AI Lab has found, machine-learning algorithms are often insufficient on their own, particularly when dealing with multiple constraints that must be satisfied.

关键词： machine learning algorithms Automation Microprocessors machine learning Packaging Iterative methods Chip scale packaging Software tools

来源：评论

学校读者我要写书评

暂无评论

Non-destructive methodology for crack detection using machine learning-assisted resonant sensor

引用

MEASUREMENT 2024年 229卷

作者： Srivastava, Rajat Vaishnav, Ajay Kale, S. N. Def Inst Adv Technol DIAT Dept Appl Phys Pune 411025 India Def Inst Adv Technol DIAT Sch Robot Pune 411025 India

Condition monitoring of structures built using metallic supports is a significant issue in structural engineering. Through this work, design, fabrication, and optimization of a dual -scale resonance -based microwave sensor is discussed for monitoring cracks developed over metallic surfaces. These sensors were initially simulated and later fabricated in micro -wave (4.59 GHz) and radio -wave region (418 MHz) for which the power attenuation and frequency shifts were observed. With the structure having crack depth of 2 mm, microwave sensor showed the power shift -24 %, while the radio -wave range sensors showed -9 %. The corresponding frequency shifts were 2.4 % and 0.85 %, respectively. The microwave sensor (power shift data) showed -280 % more sensitivity than the radio -wave sensor. By using dual scale, comprising of both frequency and power shifts, the microwave sensor classification was further improved to 99.72 %, as compared to their individual counterparts. A non-destructive crack detection system in microwave regime is hence demonstrated, which can be further explored for range of crack studies in metallic structures.

关键词： Resonators Sensor Crack detection Non-destructive techniques machine learning algorithms Structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

Adaptive Algorithm for Selecting the Optimal Trading Strategy Based on Reinforcement learning for Managing a Hedge Fund

引用

IEEE ACCESS 2024年 12卷 189047-189063页

作者： Belyakov, B. Sizykh, D. HSE Univ HSE Banking Inst Moscow 101000 Russia HSE Univ Grad Sch Business Moscow 101000 Russia

In hedge fund management, the ability to dynamically select optimal trading strategies is paramount for maximizing returns and mitigating risk. This paper presents a pioneering approach that integrates Reinforcement learning (RL), specifically the Proximal Policy Optimization (PPO) algorithm, into the strategy selection process for hedge fund management. Our model considers a diverse array of strategies, including Mean Reversion and Momentum, and employs advanced mathematical frameworks to evaluate and select the strategies. By leveraging RL, our algorithm learns to adaptively adjusts strategy allocations to maximize cumulative returns while adhering to the risk constraints. We demonstrate the effectiveness of our approach through extensive backtesting and validation of historical market data, demonstrating superior performance compared to traditional methods. Nevertheless, it is important to understand that training trading agents requires a considerable amount of time, computing power, and other resources. Our research offers a novel perspective on leveraging RL to optimize strategy selection in hedge fund management and underscores the potential of AI-driven approaches in finance.

关键词： Investment Heuristic algorithms Deep reinforcement learning Resource management Optimization Market research machine learning algorithms Robots Proposals Prediction algorithms Algorithmic trading artificial intelligence deep reinforcement learning hedge fund management optimal trading strategy proximal policy optimization (PPO) algorithm trading policy

来源：评论

学校读者我要写书评

暂无评论

Real-Time Human Posture Recognition Using Embedded Optical Fiber Sensors and machine learning

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Zhou, Xue Wang, Weijia Hui, Yaping Li, Xuegang Zhang, Xuenan Yan, Xin Cheng, Tonglei Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Foshan Grad Sch Innovat Foshan 528311 Guangdong Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China

Smart wearable technology is highly popular in biomedical and kinesiology fields due to its lightweight, flexible, comfortable, and adaptive nature. A smart wearable knee pad was designed using a fiber Bragg grating (FBG) flexible sensor for human posture recognition, combined with machine learning algorithms to enhance recognition accuracy. By placing three FBGs with different central wavelengths in key areas of the knee, posture recognition was realized by observing shifts in the FBG central wavelengths. Although FBG sensors are highly sensitive to small deformations, manual analysis of wavelength shifts introduces errors. Therefore, machine learning algorithms were integrated with the sensor to improve automatic posture recognition accuracy. A voting model combining eXtreme gradient boosting (XGBoost) and random forest (RF) was applied to the sensor. Experimental results showed an accuracy of up to 0.9908, with precision, recall, and F1 score reaching 0.9883, 0.9897, and 0.9886, respectively. The area under the receiver operating characteristic (ROC) curve reached 0.9966, demonstrating significant improvement over traditional machine learning models. These results highlight the superior performance of machine learning techniques in human posture recognition combined with FBG sensing technology, providing an effective solution for human posture analysis and virtual reality applications.

关键词： Fiber Bragg grating (FBG) human posture recognition machine learning algorithms real-time monitoring spectrum Fiber Bragg grating (FBG) human posture recognition machine learning algorithms real-time monitoring spectrum

来源：评论

学校读者我要写书评

暂无评论

learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

引用

NEW BIOTECHNOLOGY 2024年 81卷 20-31页

作者： Bernardini, Luca G. Rosinger, Christoph Bodner, Gernot Keiblinger, Katharina M. Izquierdo-Verdiguier, Emma Spiegel, Heide Retzlaff, Carl O. Holzinger, Andreas FFoQSI Technopk 1 Haus D A-3430 Tulln An Der Donau Austria Univ Nat Resources & Life Sci BOKU Vienna Inst Agron Konrad Lorenz Str 24 A-3430 Tulln An Der Donau Austria Univ Nat Resources & Life Sci BOKU Vienna Inst Soil Res Peter Jordan Str 82 A-1190 Vienna Austria Univ Nat Resources & Life Sci Inst Geomatics Vienna BOKU Peter Jordan Str 82 A-1190 Vienna Austria Austrian Agcy Hlth & Food Safety AGES Inst Soil Hlth & Plant Nutr Spargelfeldstr 191 A-1226 Vienna Austria Univ Nat Resources & Life Sci BOKU Vienna Inst Forest Engn Human Ctr AI Lab Peter Jordan Str 82 A-1190 Vienna Austria

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional processbased modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while processbased models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.

关键词： Artificial intelligence Comparison of model performance machine learning algorithms Modeling Model ensembles Process -based models Soil organic carbon

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in Logs Using Deep learning

引用

IEEE ACCESS 2024年 12卷 176124-176135页

作者： Aziz, Ayesha Munir, Kashif Natl Univ Comp & Emerging Sci FAST Sch Comp Islamabad 44000 Pakistan Arab Open Univ Fac Comp Studies Riyadh Saudi Arabia

Detection of abnormalities is important for the security and reliability of computer systems as they heavily rely on logs to detect anomalies. The logs provide general information, errors, warnings, and debugging information. Existing approaches for detecting anomalies are sometimes inaccurate due to their limitations related to log-processing leading to loss of semantic significance. Existing approaches, like Deeplog and LogAnomaly, have restrictions in detecting irregularities in log frameworks mainly in large dynamic systems. In this paper, we propose a hybrid anomaly detection technique that combines unsupervised approaches such as Self-Organizing Maps, Bert Encoders, and Autoencoders to handle these issues. The approach improves anomaly identification accuracy by employing semantic vectors obtained by the Bert Encoder to recognize patterns with autoencoders. The evaluation results show that the proposed strategy outperforms the existing methods for various types of data including system logs, network traffic, and financial transactions.

关键词： Anomaly detection Deep learning Semantics Encoding Bidirectional control Real-time systems Data models Pipelines machine learning algorithms Clustering algorithms classification unsupervised learning deep learning

来源：评论

学校读者我要写书评

暂无评论

Validating Unsupervised machine learning Techniques for Software Defect Prediction With Generic Metamorphic Testing

引用

IEEE ACCESS 2024年 12卷 165155-165172页

作者： Chan, Pak Yuen Patrick Keung, Jacky City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

In the realm of software defect prediction, unsupervised models often step in when labelled datasets are scarce, despite facing the challenge of validating models without prior knowledge of data. Addressing this, we proposed a novel approach leveraging generic metamorphic testing to validate such models effectively, bypassing the need for expert-derived metamorphic relations. Our method identifies five categories of generic metamorphic relations, further divided into twenty-one individual generic metamorphic relations, all formulated through generic Data Mutation Operators. This framework enables us to generate follow-up datasets from the source datasets, training respective software defect prediction models. By comparing predictions between the source and follow-up software defect prediction models and identifying inconsistencies, we can assess the model's sensitivity to generic metamorphic relations as a form of validation. This approach was rigorously evaluated across twenty software defect prediction models, incorporating fourteen different machine learning algorithms and twenty high-dimensional public datasets. Remarkably, the robustness of our generic MT model was confirmed, showing substantial effectiveness in validating software defect prediction models, independent of whether they were supervised or unsupervised. Software defect prediction models, using Agglomerative clustering and Density-Based Spatial Clustering of Applications with Noise algorithms, did not violate any metamorphic relation, and nineteen software defect prediction models did not significantly violate the generic metamorphic relation "Shrinkage and Expansion". Our findings suggest that employing generic metamorphic relations, especially "Shrinkage and Expansion", can universally enhance the validation of defect prediction models. Furthermore, our model can be applied to continuously monitor software defect prediction models.

关键词： Software Predictive models machine learning Adaptation models Testing Data models machine learning algorithms Numerical models Feature extraction Mathematical models Clustering methods Defect detection Clustering machine learning metamorphic relation metamorphic testing software defect prediction validation

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial: Special Issue on Information Theoretic Methods for the Generalization, Robustness, and Interpretability of machine learning

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2025年第2期36卷 1957-1958页

作者： Chen, Badong Yu, Shujian Jenssen, Robert Principe, Jose C. Mueller, Klaus-Robert Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Vrije Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands UiT Arctic Univ Norway Machine Learning Grp N-9019 Tromso Norway Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Tech Univ Berlin Machine Learning Grp D-10623 Berlin Germany

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Satellite Range Scheduling Using a Reinforcement learning-Based Memetic Algorithm

引用

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 4073-4087页

作者： Song, Yanjie Suganthan, Ponnuthurai Nagaratnam Pedrycz, Witold Yan, Ran Fan, Dongming Zhang, Yue Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Qatar Univ Coll Engn KINDI Ctr Comp Res Doha 2713 Qatar Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Polish Acad Sci Syst Res Inst PL-00901 Warsaw Poland Istinye Univ Res Ctr Performance & Prod Anal TR-34010 Istanbul Turkiye Nanyang Technol Univ Sch Civil & Environm Engn Nanyang 639798 Singapore Beihang Univ Sch Transportat Sci Engn Beijing 10091 Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing Peoples R China Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 119077 Singapore

The rapid expansion of the satellite industry has presented numerous opportunities across various sectors and significantly transformed people's daily lives. However, the high energy consumption resulting from frequent task execution poses challenges for satellite management. Energy consumption has become an important factor to be considered in the design of future satellite management systems. The energy-efficient satellite range scheduling problem (EESRSP) aims to optimize task sequencing profits within the satellite management system while simultaneously conserving energy. To address this problem, a mixed-integer scheduling model is constructed, taking into account the energy consumption of ground stations during telemetry, tracking, and command (TT&C) operations. Then, we propose a reinforcement learning-based memetic algorithm (RL-MA) that incorporates a heuristic initialization method (HIM). The HIM enables the algorithm to rapidly generate high-quality initial solutions by leveraging task features associated with EESRSRP. RL-MA employs both population search and local search (LS) techniques to explore the satellite TT&C task plan. RL-MA incorporates two genetic operators, crossover and mutation, into the population-based search. In the LS stage, multiple random and heuristic LS operators are incorporated through an ensemble LS strategy. To improve search performance, Q-learning, a classical class of reinforcement learning (RL) methods tailored to problem characteristics, is utilized for selecting effective operators. RL dynamically adjusts LS operators based on strategy performance. Experimental results demonstrate that the proposed RL-MA can effectively generate sound solutions for EESRSP with varying task scales. Furthermore, the improvement strategies employed in the algorithm are validated to enhance the scheduling performance of RL-MA. This study reveals that integrating RL with an ensemble of LS operators can significantly enhance the algorithm's exploi

关键词： Satellites Task analysis Search problems machine learning algorithms Memetics Genetic algorithms Energy consumption Energy-efficient ensemble of local search (LS) methods heuristic memetic algorithm (MA) reinforcement learning (RL) satellite range scheduling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：