检索结果-内蒙古大学图书馆

Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Shreedevi S Ronihal Sheba Selvam Kusuma P Information Science and Engineering BNM Institute of Technology Bangalore Artificial Intelligence and Machine Learning BNM Institute of Technology Bangalore Computer Science and Engineering BNM Institute of Technology Bangalore

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Agriculture represents a vital pillar of the global economy, with crop production significantly influenced by a broad variety of Phyto pathological conditions, particularly plant leaf-diseases. The early and accurate detection of these diseases are paramount, as they have a direct impact on crop yield, which consequently influences the economic viability and market profitability of agricultural outputs. However, many farmers and agricultural practitioners often lack the specialized knowledge required for precise diagnosis of leaf diseases, resulting in suboptimal crop production and economic losses. Consequently, the development of robust and efficient disease detection methodologies is imperative for the sustainability and success of agricultural practices. This research investigates the application including sophisticated deep learning algorithms like VGG19 and CNN, to identify and classify diseases in tomato plant leaves. The developed model attains an accuracy of 99.17%, surpassing both VGG19 and CNN, with the goal of enhancing crop management strategies and optimizing overall agricultural productivity.

关键词： Deep learning Accuracy Profitability computer architecture Feature extraction Robustness Convolutional neural networks Sustainable development Crop yield Diseases

来源：评论

学校读者我要写书评

暂无评论

Mediguide:A machine learning based Smart Prescription System for Personalized Healthcare

Mediguide:A Machine Learning based Smart Prescription System...

引用

artificial intelligence and Data Engineering (AIDE), International Conference on

作者： Jeyaganesh Kumar K Mithilesh G Monisha K M Nandhini S Nithish Kumar M Department of Artificial Intelligence and Data Science M.Kumarasamy College of Engineering India Department Of Artificial Intelligence and Machine Learning M.Kumarasamy College Of Engineering India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This project uses a Decision Tree Classifier model which gives accurate health predictions based on the symptoms provided by users. The model has been trained at thousands of symptoms against diseases and thus predicts very accurately for the inputted data provided by the user. The system has an easy-to-use interface that helps users input their symptoms with ease and get instant predictions of potential diseases. In addition to predicting ailments, the application provides extensive information on the detected disease. Users are given a comprehensive breakdown of the disease, recommended medications, precautions, and personalized advice on diet and exercise regimes. It provides health insights that are meant to help users better manage their health and make better lifestyle decisions. With feedback loops, prediction accuracy and user experience can always be improved directly without worrying much about each setup. It seeks to use advanced technology to make practical health advice possible. Combining disease prediction in real time with meaningful health advice creates added value for users and encourages informed health choices. It enables users to own their health by providing them with a complete picture of their possible health problems and possible solutions to address them. This Project provides a powerful tool for more proactive users, with its combo of predictive analytics and health education resources.(And it helps us recognise the sport itself the user-friendly and detailed output that makes it easy and helpful for everyone to understand their health and what they can do about it.) It upskill traditionally trained AI learners to be perpendicular over ruinous health data towards an accessibly governed future, furnished with health facts and outcomes to improvement on the go.

关键词： Accuracy Medical services machine learning Predictive models Prediction algorithms Data models User experience Decision trees Medical diagnostic imaging Diseases

来源：评论

学校读者我要写书评

暂无评论

Incremental learning Framework for Mining Big Data Stream

引用

computers, Materials & Continua 2022年第5期71卷 2901-2921页

作者： Alaa Eisa Nora E.L-Rashidy Mohammad Dahman Alshehri Hazem M.El-bakry Samir Abdelrazek Information Systems Department Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Machine Learning and Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

At this current time,data stream classification plays a key role in big data analytics due to its enormous *** of the existing classification methods used ensemble learning,which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data,it also supposes that all data are *** weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of *** main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network *** proposed model is implemented in spark *** each data stream,the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound *** method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and *** results show that the proposed model outperforms stateof-the-art performance in terms of accuracy(0.861)precision(0.9328)and minimal MSE(0.0416).

关键词： Ant lion optimization(ALO) big data stream generative adversarial network(GAN) incremental learning renyi entropy

来源：评论

学校读者我要写书评

暂无评论

Liver Disorder Diagnosis Using machine learning Technique

Liver Disorder Diagnosis Using Machine Learning Technique

引用

science, Technology, Engineering and Management (ICSTEM), International Conference on

作者： Aneesh Nemalikanti Ebenezer Jangam Computer Science and Engineering Karunya Institute Of Technology and Sciences Coimbatore India Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The utilization of machine learning techniques in diagnosing liver disorders has garnered considerable attention for its potential to enhance accuracy and efficiency. However, in this study, we pivot our focus towards developing a predictive model for detecting liver tumors, leveraging patient information encompassing demographic details, medical history, and laboratory test results. By employing advanced algorithms such as Efficient-Net and LeNet, we aim to classify whether the input data indicates the presence of a liver tumor. This novel approach has demonstrated improved accuracy compared to traditional methods, marking a significant advancement in liver tumor diagnosis. The outcomes demonstrate how well our suggested methodology works to reliably identify liver tumors. thereby potentially aiding healthcare professionals in making timely and precise decisions, ultimately leading to enhanced patient care.

关键词： Accuracy Liver Medical services machine learning Predictive models Prediction algorithms User experience

来源：评论

学校读者我要写书评

暂无评论

Forecast Model for Scheduling an HPC Application on CPU and GPU Architecture 3

Forecast Model for Scheduling an HPC Application on CPU and ...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Chandrashekhar, B.N. Mohan, M. Geetha, V. BMS Institute of Technology & Management Artificial Intelligence and Machine Learning Benguluru India Nitte Meenakshi Institute of Technology Information Science & Engineering Benguluru India Reva University School of Management Bengaluru India

ISBN: (纸本)9798350338607

Process scheduling is an essential part of multiprogramming operating systems. Scheduling is a process that allows one process to use the processing unit while the execution of another process is on hold (in a waiting state) due to the unavailability of any resource like I/O, thereby making full use of CPU or GPU. The major issue of scheduling is to make the system efficient, fast, and fair. This work focuses on developing a Forecast model and constructing scheduling strategies to schedule parallel applications on CPU and GPU. During the design of the Forecast model, we will consider the history of the actual execution time set of processes, then we compute the average time of individual sets of processes by considering parameters such as complete execution time, the sum of processes, and the number of threads assigned to individual processes. Then we will evaluate the Prediction time of CPU and GPU for individual sets of processes. By considering parameters such as the average time of the previous set of processes, the weight of processes, and the number of processes. Then based on the prediction time we will develop a scheduling strategy. As the minimum prediction time required set of process resources is assigned to the CPU and the GPU is assigned by the maximum predicted timed resource of the process. In this work we utilized the CPU and GPU resources effectively for stream benchmark application, our experiment shows that less than 20% average percentage prediction error in all cases. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

UCPM: Uncertainty-Guided Cross-Modal Retrieval with Partially Mismatched Pairs

引用

IEEE Transactions on Image Processing 2025年 34卷 3622-3634页

作者： Zha, Quanxing Liu, Xin Cheung, Yiu-Ming Peng, Shu-Juan Xu, Xing Wang, Nannan Huaqiao University Department of Computer Science Xiamen 361021 China Key Laboratory of Pattern Recognition and Computer Vision Xiamen 361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021 China Hong Kong Baptist University Department of Computer Science Hong Kong Huaqiao University Department of Artificial Intelligence Xiamen 361021 China Fujian Province University Key Laboratory of Computer Vision and Machine Learning (Huaqiao University) Xiamen 361021 China University of Electronic Science and Technology of China Center for Future Multimedia School of Computer Science and Engineering Chengdu 610051 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an 710071 China

The manual annotation of perfectly aligned labels for cross-modal retrieval (CMR) is incredibly labor-intensive. As an alternative, the collection of co-occurring data pairs from the Internet is a remarkably cost-effective way, but which, inevitably induces the Partially Mismatched Pairs (PMPs) and therefore significantly degrades the retrieval performance without particular treatment. Previous efforts often utilize the pair-wise similarity to filter out the mismatched pairs, and such operation is highly sensitive to mismatched or ambiguous data and thus leads to sub-optimal performance. To alleviate these concerns, we propose an efficient approach, termed UCPM, i.e., Uncertainty-guided Cross-modal retrieval with Partially Mismatched pairs, which can significantly reduce the adverse impact of mismatched data pairs. Specifically, a novel Uncertainty Guided Division (UGD) strategy is sophisticatedly designed to divide the corrupted training data into confident matched (clean), easily-identifiable mismatched (noisy) and hardly-determined hard subsets, and the derived uncertainty can simultaneously guide the informative pair learning while reducing the negative impact of potential mismatched pairs. Meanwhile, an effective Uncertainty Self-Correction (USC) mechanism is concurrently presented to accurately identify and rectify the fluctuated uncertainty during the training process, which further improves the stability and reliability of the estimated uncertainty. Besides, a Trusted Margin Loss (TML) is newly designed to enhance the discriminability between those hard pairs, by dynamically adjusting their soft margins to amplify the positive contributions of matched pairs while suppressing the negative impacts of mismatched pairs. Extensive experiments on three widely-used benchmark datasets, verify the effectiveness and reliability of UCPM compared with the existing SOTA approaches, and significantly improve the robustness in both synthetic and real-world PMPs. The code i

关键词： Cross-modal retrieval partially mismatched pairs trusted margin loss uncertainty guided division uncertainty self-correction

来源：评论

学校读者我要写书评

暂无评论

Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

引用

Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Charanya P Nidhish M Prajesh U R Saktheeswar K Sanjana S Pillai Department of Artificial Intelligence and Machine Learning Sri Eshwar College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being.

关键词： Sentiment analysis Technological innovation Engineering profession Organizations Mental health Solids Real-time systems Mentoring artificial intelligence Monitoring

来源：评论

学校读者我要写书评

暂无评论

Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention 4

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balassem, Zayd Srikanteswara, Ramya Esakki Madura, E. Anjaneyulu, Madugula Devi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed-established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM. © 2024 IEEE.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Multi-Keywords Based Fully Homomorphic Encryption and Data Classification for Cloud Security and Privacy

Multi-Keywords Based Fully Homomorphic Encryption and Data C...

引用

Emerging Trends in Engineering and Medical sciences (ICETEMS), International Conference on

作者： Kooragayala Sukeerthi R. Kesavan S.A. Kalaiselvan Department of Computer Science & Engineering Saveetha School of Engineering SIMATS Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai

ISBN: (数字)9798331529352

ISBN: (纸本)9798331529390

When it comes to processing and storing massive amounts of data efficiently, cloud computing has become the dominant paradigm. Both consumers and suppliers of services in cloud settings are understandably worried about the safety of critical data. When it comes to protecting sensitive information during transmission in the cloud, homomorphic encryption has shown to be an effective method. This study presents a new homomorphic encryption method for efficient and safe computations on the cloud, determined by multistage partial homomorphic encryption. The learning using errors (LWE) issue, primarily an altered form of the BVG method, is the basis of the proposed completely homomorphic encryption technique. It is believed to be difficult to resolve the learning with mistakes issue, which is a challenge in machine learning. We were able to implement the technique by making use of MFHE, an encryption method that can process inputs encrypted under different keys. Our expansion of the technique to search for several keywords was based on this. To make data processing in the cloud safe and privacy protecting, the suggested system combines the strengths of multistage encryption with partial homomorphic encryption.

关键词： Cloud computing Data privacy Privacy machine learning Data processing Market research Safety Servers Homomorphic encryption Secure storage

来源：评论

学校读者我要写书评

暂无评论

Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populations 40

Stable Heterogeneous Treatment Effect Estimation across Out-...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Yuling Wu, Anpeng Kuang, Kun Du, Liang Sun, Zixun Wang, Zhi Tsinghua University Tsinghua Shenzhen International Graduate School China Zhejiang University College of Computer Science and Technology China Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department United Arab Emirates Interactive Entertainment Group Tencent Tsinghua University Tsinghua-Berkeley Shenzhen Institute China

ISBN: (纸本)9798350317152

Heterogeneous treatment effect (HTE) estimation is vital for understanding the change of treatment effect across individuals or subgroups. Most existing HTE estimation methods focus on addressing selection bias induced by imbalanced distributions of confounders between treated and control units, but ignore distribution shifts across populations. Thereby, their applicability has been limited to the in-distribution (ID) population, which shares a similar distribution with the training dataset. In real-world applications, where population distributions are subject to continuous changes, there is an urgent need for stable HTE estimation across out-of-distribution (OOD) populations, which, however, remains an open problem. As pioneers in resolving this problem, we propose a novel Stable Balanced Representation learning with Hierarchical-Attention Paradigm (SBRL-HAP) framework, which consists of 1) Balancing Regularizer for eliminating selection bias, 2) Independence Regularizer for addressing the distribution shift issue, 3) Hierarchical-Attention Paradigm for coordination between balance and independence. In this way, SBRL-HAP regresses counterfactual outcomes using ID data, while ensuring the resulting HTE estimation can be successfully generalized to out-of-distribution scenarios, thereby enhancing the model's applicability in real-world settings. Extensive experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of our SBRL-HAP in achieving stable HTE estimation across OOD populations, with an average 10% reduction in the error metric PEHE and 11% decrease in the ATE bias, compared to the SOTA methods. © 2024 IEEE.

关键词： Population distribution

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：