检索结果-内蒙古大学图书馆

24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Hsueh, Chun-Yu Zhang, Yu Lu, Yu-Wei Han, Jen-Chieh Meesawad, Wilailack Tsai, Richard Tzong-Han Department of Computer Science and Information Engineering National Central University Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taiwan

In this paper, we present our system applied in BioASQ 11b phase b. We showcase prompt engineering strategies and outline our experimental steps. Building upon the success of ChatGPT/GPT-4 in answer generation and the field of biology, we developed a system that utilizes GPT-4 to answer biomedical questions. The system leverages OpenAI’s ChatCompletions API and combines Prompt engineering methods to explore various prompts. In addition, we also attempted to incorporate GPT-4 into our system from last year, which combines a BERT-based model and BERTScore. However, the standalone GPT-4 method outperformed this approach by a large margin. Ultimately, in our submission, we adopted what we believe to be the optimal prompts and achieved the highest scores in the second batch. © 2023 Copyright for this paper by its authors.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Cyber-Immunity at the Core: Securing Biomedical Devices through Hardware-Level Machine Learning Defense

Cyber-Immunity at the Core: Securing Biomedical Devices thro...

引用

2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Sayadi, Hossein He, Zhangying Fernandes, Chelsea William Miari, Tahereh California State University Department of Computer Engineering and Computer Science Long BeachCA United States Claremont Graduate University Center for Information Systems and Technology CA United States

ISBN: (纸本)9798350300260

Healthcare cybersecurity is crucial for protecting hospitals' networks and computing systems from malicious cyber-attacks. With the increasing motivation and capability of cyber attackers, it is necessary to secure the software and hardware infrastructure of biomedical computing systems used in healthcare. Due to the increasingly digitized nature of modern biomedical devices used in healthcare systems, effective cybersecurity solutions have become more essential. Traditional security protocols focused on software-level protection, with the underlying hardware assumed to be secure. However, recent studies have revealed vulnerabilities in hardware that attackers can exploit to compromise computing systems. To address this, in this paper a bottom-up approach is proposed, delegating security to the underlying hardware to enhance the security and cost-efficiency with less visibility to the attacker. This paper aims to develop effective intelligent countermeasures for hardware-assisted cyber-security at run-time in biomedical devices. It leverages processors' performance events via Hardware Performance Counters (HPCs) combined with Machine Learning (ML) techniques to fortify biomedical devices at the hardware level. To achieve this objective, we introduce a tailored and cost-effective hardware monitoring framework, complemented by a machine learning-enabled methodology. This approach enhances the accuracy and efficiency of malware detection and identification using real-time data from biomedical processors' hardware events. The experimental results demonstrate that the XGBoost model, using 4 hardware events, excels in malware detection, boasting 95% detection rate (F-measure and Accuracy), efficient resource utilization, and low inference latency. Additionally, the ExtraTree classifier achieves 87% F-measure and exhibits swift performance in classifying malware types in real-time. © 2023 IEEE.

关键词： Biomedical Cybersecurity Hardware Security Machine Learning Malware Detection and Identification

来源：评论

学校读者我要写书评

暂无评论

Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

引用

8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Smart Home Camera Fall Detection System 13th

Smart Home Camera Fall Detection System

引用

13th International Conference on computer engineering and networks, CENet 2023

作者： Ding, Ziqi Qian, Hanwei Wu, Zechen Liu, Wei Department of Computer Information and Network Security Jiangsu Police Institute Nanjing China Engineering Research Center of Electronic Data Forensics Analysis Jiangsu Province Nanjing China City University of Macau China

ISBN: (纸本)9789819992386

As China’s population ages, the issue of population aging has become increasingly severe. By the end of 2022, the population aged 60 and above in China has already reached 280 million, with an estimated 25 million falls occurring annually. Falls pose a serious threat to the health and well-being of the elderly. To address this problem, this article presents the design of an intelligent home camera fall detection system aimed at detecting falls and providing immediate assistance. The design incorporates streaming technology and image processing techniques, utilizing a Raspberry Pi camera placed in the home to collect data and stream it via RTMP. A remote server then pulls the video stream and performs fall detection using a combination of the Tiny-YOLOv3, AlphaPose, and ST-GCN algorithms. Upon detecting a fall, the system sends an immediate alert to designated individuals via the PushPlus platform, ensuring timely warnings and effectively safeguarding the safety of the elderly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Fall detection

来源：评论

学校读者我要写书评

暂无评论

Customer Retention Using Machine Learning 10

Customer Retention Using Machine Learning

引用

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Das, Lipsa Salman, Rahama Sabeer, Shaista Ansari, Subuhi Kashif Aarif, Mohd Rana, Ajay Amity School of Engineering and Technology Amity University UP Greater Noida India Jazan University Department of Computer Science Saudi Arabia Global Research Network UP Noida India College of Computer Science & Information Technology Jazan University Department of Information Technology and Security Jazan Saudi Arabia College of Computer Science and Information Technology & Security Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350382471

Maintaining customer retention stands as a pivotal element in ensuring business success, given that the costs associated with acquiring new customers often surpass those linked to retaining existing ones. Recognizing the significance of retaining a loyal customer base, businesses prioritize strategies and initiatives that cultivate lasting relationships with their current clientele, understanding that sustained customer loyalty contributes substantially to long-term viability and profitability.. Machine learning techniques offer a promising approach to predicting customer behavior and developing personalized retention strategies. In this research paper, we explore the use of machine learning for customer retention, specifically focusing on predictive modeling and recommendation systems. We review recent literature on the topic, discussing different machine learning algorithms and techniques that have been applied to customer retention. We also present a case study of a company that has successfully implemented machine learning-based retention strategies, highlighting the benefits and challenges of this approach. Our findings suggest that machine learning can be an effective tool for improving customer retention, but its success depends on several factors, including data quality, model accuracy, and implementation strategy. Our conclusion involves an exploration of the consequences of our research for forthcoming studies and practical applications within business environments.. © 2023 IEEE.

关键词： customer behavior Customer retention machine learning predictive modelling

来源：评论

学校读者我要写书评

暂无评论

Academic Evaluation System Using Adaptive Categorized and Authenticated Recommendation 21

Academic Evaluation System Using Adaptive Categorized and Au...

引用

21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Haddad, Haider Abdula Abdullah, Abdulqadir Ismail Mahdy, Qaysar Salih Kahwachi, Wasfi College of Science Salahaddin University Erbil Department of Computer Science and Information Technology Kurdistan Region Iraq College of Engineering Knowledge University Erbil Department of Computer Engineering Kurdistan Region Iraq College of Engineering Tishk International University Erbil Department of Mechatronics Engineering Kurdistan Region Iraq Research Center Tishk International University Erbil Kurdistan Region Iraq

ISBN: (纸本)9798350374131

Teacher performance is an important issue for any academic institution, so these institutions try to develop methodologies and systems to evaluate the performance of its academic staff based on measurable and scientific metrics. These metrics include collecting data from students about classes and how the teacher is performing in the classroom and measuring the learning objectives from students. Major objective of this research work is to develop a web-based multi-user system for academic performance and a recommendation system that depends on the performance and skills of the teacher. The system includes multiple entry points for best factor such as the feedback unit, student attendance unit, teacher classification unit, and teacher recommendation unit, which can be strictly accessed by the administrative authority for academic teachers. The attendance module and the student feedback module are required to achieve better results in the teacher rating module and the teacher recommendation module. A reliability factor has been incorporated to achieve the accuracy of the proposed system. The performance of the system was improved due to the integration of the paperless approach and resulted in less data loss. The developed system was tested for a large university in Kurdistan region, Iraq. For data analysis an adaptive unsupervised learning method was used to categorize teachers according to their performance in the classroom. The system proved very successful in the academic evaluation and recommendation process in the university. This research proved that using student feedback data is the best factor in determining academic performance. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Local and Global Feature-Aware Dual-Branch networks for Plant Disease Recognition

引用

植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

引用

computer Systems science & engineering 2023年第5期45卷 2093-2109页

作者： Sureshkumar Rajagopal Tamilvizhi Thanarajan Youseef Alotaibi Saleh Alghamdi Center for System Design Chennai Institute of TechnologyChennai600069India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease *** a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation *** is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed *** Convolutional Neural networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain *** two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise *** U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going *** the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour ***,the estimates was made by two separate models that were put together to produce the final *** comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

关键词： Medical imaging segmentation U-net 3D CNN brain tumor

来源：评论

学校读者我要写书评

暂无评论

Analytical Study of the Features and Parameters of Off-Angle Iris Image vs. Deep Learning Model

Analytical Study of the Features and Parameters of Off-Angle...

引用

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Suleiman, Aghiad Khedr Shafek, Darin Mahfuri, Mahmoud Khishe, Mohammad Saeed, Ali Ghwanmeh, Sameh Alma'Moon University College Cybersecurity and Cloud Computing Technologies Engineering Dept. Baghdad Iraq Alma'Moon University College Computer Engineering Techniques Dept. Baghdad Iraq International University for Science and Technology Computer and Information Technology Department Syria Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara Research Center Jadara University Jordan Jordan Computer Center Northern Technical University Ninevah Iraq College of Computer Information Technology Computer Science Dept American University in the Emirates United Arab Emirates

ISBN: (纸本)9798350369106

This paper presents an analytical study and a technique for extracting the features of a common case of images of the iris called off-angle iris which was taken for persons identification systems. The main problem when using biological iris measurements to identify the persons is the difficulty of identifying and extracting features of the iris. This problem is increasing when dealing with off-angle iris and it leads to decreased system accuracy and increased system rate error. In return, all the transfer learning techniques face difficulties in the case of heavily degraded data and partially occluded, and off-angle. A new method has a whole new methodology to deal with the image as it is without transformation processes. A new algorithm has been included for extracting features of the iris through the pupil switching points. The most discriminating points of the iris depend on the biological human eye statistics and analytical study. It has been trained and tested on the common images of the off-angle iris database so-called: 'CASIA Iris 1.0'. It has been implemented in the MATLAB environment. The results showed the efficiency of this technique, high precision and more importantly low failed acceptance rate. It emphasized that it is adaptive as well as efficiency improvement of the system. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection and Classification Using PSO and Convolutional Neural network

引用

computers, Materials & Continua 2022年第12期73卷 4501-4518页

作者： Muhammad Ali Jamal Hussain Shah Muhammad Attique Khan Majed Alhaisoni Usman Tariq Tallha Akram Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadWah CampusPakistan Departmnt of Computer Science HITEC University TaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of EE COMSATS University IslamabadWah CampusPakistan Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Tumor detection has been an active research topic in recent years due to the high mortality *** vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI *** automated detection process is simpler and takes less time than manual *** addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for *** proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this *** tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)*** preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation ***,PSO-based segmentation is *** the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer *** a serial approach,features are extracted from both trained models and fused features for final *** classification,a variety of machine learning classifiers are *** dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of ***,the proposed method is compared to state-of-the-art existingmethods and outperforms them.

关键词： Magnetic resonance imaging(MRI) tumor segmentation deep learning features extraction classification

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：