检索结果-内蒙古大学图书馆

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

引用

Neural computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A Novel Clustering Framework to Identify Team Playing Styles Within Australian Football

引用

SN Computer Science 2025年第3期6卷 1-11页

作者： Moffatt, Samuel J. Gupta, Ritu Rakshit, Suman Keller, Brad S. School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Curtin Biometry and Agricultural Data Analysis Centre for Crop and Disease Management Curtin University Perth Australia Fremantle Football Club Perth Australia

A novel clustering framework is proposed to identify playing styles in invasion sports, particularly Australian football, based on match transactional data. These data are segmented at a granular level, representing an intrinsic partitioning of the entire match period into short-time interval event data. Clustering based on such granular time-event data provides opportunities to identify team playing styles at different periods within a match. Identifying effective team playing styles over short time intervals can be crucial for improving overall match strategies. Due to the complexity involved in forming interpretable clusters from vast amounts of data, this research has not been attempted before. As a key component of the clustering framework, this paper introduces a composite clustering assessment index, enabling the selection of the optimal clustering algorithm and the appropriate number of clusters. The composite index integrates subject matter expert knowledge, ensuring that the resulting clusters are not only data-driven but also aligned with the goals of the analysis. The framework is applied to identify offensive playing styles within Australian football by clustering possession chain data of offensive phases from 414 matches during the 2021 and 2022 Australian Football League (AFL) seasons. A total of 27 features from 112,530 offensive possession chains were clustered, identifying five distinct offensive team playing styles: (i) ‘Ineffective Ball Movers’, (ii) ‘Uncontested Ball Movers’, (iii) ‘Direct Ball Movers’, (iv) ‘Handball Ball Movers’, and (v) ‘Kick Ball Movers’. © The Author(s) 2025.

关键词： Clustering assessment index Hierarchical agglomeration k-means k-medoids Playing style Team sport

来源：评论

学校读者我要写书评

暂无评论

Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

引用

2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Contrastive-Based Removal of Negative Information in Multimodal Emotion Analysis

引用

Cognitive Computation 2025年第3期17卷 1-16页

作者： Wang, Rui Wang, Yaoyang Cambria, Erik Fan, Xuhui Yu, Xiaohan Huang, Yao E, Xiaosong Zhu, Xianxun School of Communication and Information Engineering Shanghai University Shanghai China College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Computing Macquarie University Sydney Australia

Multimodal sentiment analysis bridges the communication gap between humans and machines by accurately recognizing human emotions. However, existing approaches often focus on synchronizing multimodal data to enhance accuracy, overlooking the critical role of negative information. Negative information generally refers to noise or inconsistencies with primary emotional labels within the data, such as disparities in emotional expressions across different modalities and noisy data elements. These issues can significantly compromise the effectiveness of sentiment analysis systems. To address this challenge, we propose a novel method based on contrastive learning for the removal of non-relevant features within single modalities, aiming to eliminate negative information in speech, text, and image data. Additionally, we have designed an enhanced multi-head attention mechanism that integrates the cleansed features into a unified representation for emotion analysis. Experimental evaluations on the CMU-MOSI and CMU-MOSEI datasets demonstrate that our method significantly outperforms existing approaches in sentiment analysis tasks. This method not only improves accuracy but also ensures the system’s robustness against the diverse and noisy nature of real-world data. The relevant code is available at https://***/YaoYangWang/MECAM .

关键词：

来源：评论

学校读者我要写书评

暂无评论

TERM: Tree Ensemble Models for Interpretable Rule Mining 25th

TERM: Tree Ensemble Models for Interpretable Rule Mining

引用

25th International Conference on Web Information Systems engineering, WISE 2024

作者： Zhu, Mengyu Li, Zhao Zhao, Jun Liu, Xiaopeng Liu, Yongning School of Information Engineering Ningxia University Yinchuan China College of Intelligence and Computing Tianjin University Tianjin China School of Economics and Management Ningxia University Yinchuan China Financial Big Data Laboratory Bank of Shizuishan Yinchuan China

ISBN: (纸本)9789819605781

Ensemble learning, particularly tree-based ensemble techniques, is acknowledged as the advanced approach to solving a wide range of challenging issues because of its exceptional performance in multiple machine learning applications. Nonetheless, even with its capacity for prediction, ensemble learning’s interpretability continues to be a problem, which restricts its use in situations where decision transparency is necessary. We suggest a novel model named TERM (Tree Ensemble Models for Interpretable Rule Mining) in order to get over this restriction and enhance the interpretability of Ensemble Tree Models. This method converts the complex binary classification decision-making process inside tree ensemble models into a series of rule lists that are globally equivalent to the decision-making process of the original model. This transformation aids in a more precise understanding and interpretation of the model’s predictive outcomes, enhancing its interpretability. Compared with existing rule extraction methods, TERM can simultaneously be applied to bagging methods and boosting methods. During the rule extraction phase, an adaptive set cover algorithm ADAPT-TA, is proposed to select rule subsets from the original rule set while avoiding local optima. Additionally, this method reduces the generalization error in the set of rules and runs faster on ensemble models involving multiple decision trees. Through comparisons with six other models, we evaluated the performance and applicability of TERM, demonstrating its outstanding accuracy and interpretability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Cooperative Multiagent Learning and Exploration With Min-Max Intrinsic Motivation

引用

IEEE Transactions on Cybernetics 2025年第6期55卷 2852-2864页

作者： Hou, Yaqing Kang, Jie Piao, Haiyin Zeng, Yifeng Ong, Yew-Soon Jin, Yaochu Zhang, Qiang Dalian University of Technology College of Computer Science and Technology Dalian116024 China Jilin University School of Artificial Intelligence Changchun130015 China Northumbria University Newcastle upon TyneNE1 8ST United Kingdom Nanyang Technological University School of Computing and Data Science Singapore Singapore Agency for Science Technology and Research Centre for Frontier Ai Research Institute of High Performance Computing Singapore Singapore Westlake University School of Engineering Hangzhou310030 China

In the field of multiagent reinforcement learning (MARL), the ability to effectively explore unknown environments and collect information and experiences that are most beneficial for policy learning represents a critical research area. However, existing work often encounters difficulties in addressing the uncertainties caused by state changes and the inconsistencies between agents' local observations and global information, which presents significant challenges to coordinated exploration among multiple agents. To address this issue, this article proposes a novel MARL exploration method with Min-Max intrinsic motivation (E2M) that promotes the learning of joint policies of agents by introducing surprise minimization and social influence maximization. Since the agent is subject to unstable state changes in the environment, we introduce surprise minimization by computing state entropy to encourage the agents to cope with more stable and familiar situations. This method enables surprise estimation based on the low-dimensional representation of states obtained from random encoders. Furthermore, to prevent surprise minimization from leading to conservative policies, we introduce mutual information between agents' behaviors as social influence. By maximizing social influence, the agents are encouraged to interact to facilitate the emergence of cooperative behavior. The performance of our proposed E2M is testified across a range of popular StarCraft II and Multiagent MuJoCo tasks. Comprehensive results demonstrate its effectiveness in enhancing the cooperative capability of the multiple agents. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Towards High-throughput and Low-latency Billion-scale Vector Search via CPU/GPU Collaborative Filtering and Re-ranking 23

Towards High-throughput and Low-latency Billion-scale Vector...

引用

23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present FusionANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of FusionANNS lies in CPU/GPU collaborative filtering and re-ranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. © 2025 FAST. All Rights Reserved.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Localization-Assisted Fast and Robust Beam Optimization for mmWave Communications

引用

IEEE Internet of Things Journal 2025年第9期12卷 12383-12396页

作者： Xiao, Qiqi He, Yinghui Yu, Guanding Yuan, Jiantao Yin, Rui Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore Hangzhou City University School of Information and Electrical Engineering Academy of Edge Intelligence Hangzhou310015 China Zhengzhou Digital Industry Institute Hangzhou City University Zhengzhou450046 China Hangzhou City University School of Information and Electrical Engineering Hangzhou310015 China

The millimeter wave (mmWave) communication becomes a key enabler for the future Internet of Things (IoT) due to its capability for supporting high rate and low-latency traffic. However, beamforming in the mmWave band faces issues of low efficiency since the narrow beam of mmWave devices would increase the search delay and overhead. Inspired by this, we utilize localization over sub-6 GHz band to assist the mmWave base station in performing fast and robust adaptive beamforming (RABF). Different from existing works, we focus on the indoor scenario and consider the effects of several practical issues, including localization errors and hardware defects. Specifically, a novel two-step access scheme is proposed. During the first step, we design a novel localization method customized for indoor scenarios, jointly considering the time of flight and angle of arrival. The localization error is further analyzed to determine the mmWave scanning angle and an optimal beamwidth expression is derived in closed-form to maximize system throughput with the considerations of the search delay. Moreover, considering the mismatch of the steering vector caused by the hardware defects, we propose an RABF method in closed-form. Simulation results demonstrate that the proposed scheme can effectively reduce the search delay and realize robust beamforming to enhance the mmWave communication performance. © 2014 IEEE.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

引用

SN Computer Science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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：