检索结果-内蒙古大学图书馆

15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Venkatesh, Swarna Saritha, Kuppala Gogula, Sreenivasulu Chitteti, Chengamma Yalavarthi, Sikhi Swaraja, K. ECE CMR Technical Campus Hyderabad India CSE Presidency University Bangalore India Vardhaman College of Engineering Hyderabad India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupati India Lexisnexis Risk Solutions Inc. Oklahomacity United States ECE GRIET Hyderabad India

ISBN: (纸本)9783031810824

Alzheimer's disease occurs due to neurodegenerative disorder, innovative approach is needed for early detection of disease. A method combining multimodal data fusion with adaptive capabilities of Adam optimization algorithm to enhance Alzheimer's disease prediction have been proposed. The proposed multimodal fusion approach combines neuroimaging, clinical, and genetic data into a deep neural network architecture, in order to achieve better accuracy,. Utilization of multimodal data helps to capture the view of disease, from structural brain changes to clinical assessments. This approach not only enhances predictive accuracy but also provides valuable insights into the mechanisms of Alzheimer's disease prediction. Utilization of Adam's optimization algorithm helps the model to converge faster making the proposed method highly effective. Combining the strengths of multimodal fusion and Adam optimization, the proposed method can be utilized for early Alzheimer's disease diagnosis, leading to improved patient outcomes and improved accuracy in Alzheimer's disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Can Large Language Models Predict the Outcome of Judicial Decisions?

arXiv

引用

arXiv 2025年

作者： Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Al-Ghraibah, Amani Computer Science and Engineering Dept. Qatar University Doha Qatar School of Computing and Data Science OUC in partnership with LJMU Doha Qatar Biomedical Engineering Dept. Jordan University of Science and Technology Irbid Jordan

Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.23B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using LoRA. Additionally, we employed a comprehensive evaluation framework that integrates both quantitative metrics (such as BLEU, ROUGE, and BERT) and qualitative assessments (including Coherence, Legal Language, Clarity, etc.) using an LLM. Our results demonstrate that fine-tuned smaller models achieve comparable performance to larger models in task-specific contexts while offering significant resource efficiency. Furthermore, we investigate the impact of fine-tuning the model on a diverse set of instructions, offering valuable insights into the development of a more human-centric and adaptable LLM. We have made the dataset, code, and models publicly available to provide a solid foundation for future research in Arabic legal NLP1 Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Convergent Differential Privacy Analysis for General Federated Learning: the f-DP Perspective

arXiv

引用

arXiv 2024年

作者： Sun, Yan Shen, Li Tao, Dacheng Faculty of Engineering The University of Sydney Australia School of Cyber Science and Technology Sun Yat-sen University China College of Computing & Data Science Nanyang Technological University Singapore

Federated learning (FL) is an efficient collaborative training paradigm extensively developed with a focus on local privacy, and differential privacy (DP) is a classical approach to capture and ensure the reliability of private security. Their powerful cooperation provides a promising paradigm for the large-scale private clients. As a predominant implementation, the noisy perturbation has been widely studied, being theoretically proven to offer significant protections. However, existing analyses in FL-DP mostly rely on the composition theorem and cannot tightly quantify the privacy leakage challenges, which is tight for a few communication rounds but yields an arbitrarily loose and divergent bound eventually. This also implies a counterintuitive judgment, suggesting that FL-DP may not provide adequate privacy support during long-term training. To further investigate the convergent privacy and reliability of the FL-DP framework, in this paper, we comprehensively evaluate the worst privacy of two classical methods under the non-convex and smooth objectives based on the f-DP analysis. With the aid of the shifted interpolation technique, we successfully prove that privacy in Noisy-FedAvg has a tight convergent bound. Moreover, with the regularization of the proxy term, privacy in Noisy-FedProx has a stable constant lower bound. Our analysis further demonstrates a solid theoretical foundation for the reliability of privacy in FL-DP. Meanwhile, our conclusions can also be losslessly converted to other classical DP analytical frameworks, e.g. (ϵ,δ)-DP and Rényi-DP (RDP). © 2024, CC BY.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Navigating Grid Dynamics with V2G Technology and Naive Bayes Optimization in IoT Systems 3

Navigating Grid Dynamics with V2G Technology and Naive Bayes...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Arulprakash, M. Raman, Ramakrishnan Gokhale, Atul A. Saravanan, K. Ishwarya, M.V. Sujatha, S. Srm Institute of Science and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Pune India Symbiosis School of Culinary Arts Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

Vehicle-to-Grid (V2G) technology and Naive Bayes optimization, which might help IoT devices, navigate grid dynamics. Electric vehicles (EVs) with bidirectional charging capability may provide grid assistance using their energy storage capacity. These cars can take and return energy from the grid using V2G technology, stabilizing the system. This paper proposes a unique technique to intelligently control energy flow between the grid and EVs using V2G technology and Naive Bayes optimization algorithms. Using previous data on energy consumption, grid circumstances, and vehicle charging behavior, the Naive Bayes algorithm makes real-time predictions and decisions. In IoT-enabled smart grids, this connection improves energy management efficiency and reliability. The implementation methods and experimental findings of the suggested strategy are discussed in the study. The system grid stability, energy usage, and grid dynamics management are also assessed. The results show that V2G technology and Naive Bayes optimization in IoT systems can intelligently navigate grid dynamics, creating a more sustainable and resilient energy infrastructure. To smart grid expertise and shows how V2G-enabled IoT devices may optimize energy distribution and ensure grid stability in the face of dynamic energy needs. © 2024 IEEE.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Social Media and Stock Market Prediction: A Big data Approach

引用

Computers, Materials & Continua 2021年第5期67卷 2569-2583页

作者： Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz School of Computing Faculty of EngineeringUniversity Teknologi MalaysiaJohorMalaysia Department of Software Engineering University of Management and TechnologyLahorePakistan Collage of Business Abu Dhabi UniversityAbu DhabiUnited Arab Emirates Oxford Center for Islamic Studies The University of OxfordOxfordUK The University of Liverpool Management School The University of LiverpoolLiverpoolUK Department of Computer Engineering National University of TechnologyIslamabadPakistan School of Computing UTM Big Data CentreUniversiti Teknologi MalaysiaJohorMalaysia

Big data is the collection of large datasets from traditional and digital sources to identify trends and *** quantity and variety of computer data are growing exponentially for many *** example,retailers are building vast databases of customer sales *** are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and *** of big data include volume and variety in both structured and unstructured *** this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional *** studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision *** implemented naive Bayes and logistic regression classification *** results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

关键词： Big data analytics artificial intelligence machine learning stock market social media business analytics

来源：评论

学校读者我要写书评

暂无评论

Converter Networks for Multi-Scale Tokens-Aware Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model 2

Converter Networks for Multi-Scale Tokens-Aware Multi-Region...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Mahalle, Parikshit N. Chandra Lohani, Manoj Hemelatha, S. Sharma, Divya Chitra, S. Jayakumar, Sujayaraj Samuel Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India Graphic Era Hill University School of Computing Haldwani India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India School of Sciences Forensic Science Karnataka Bangalore India

ISBN: (纸本)9798350388916

Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) streamlines the planning of radiation therapy treatments by doing away with CT simulation and error-prone picture registration, which in turn reduces radiation dosage for patients and setup uncertainty. To create sCT images that matched the MRI anatomy, an optimally trained MultiHead Attention was used in conjunction with the reverse diffusion process. We generated sCT using MRI on two datasets - one for the brain and one for the prostate - to test the suggested strategy. The following metrics were used for qualitative evaluation: ground truth CTs and sCTs normalized cross correlation (NCC) indices, multi-scale Structure Similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) of Hounsfield unit (HU). A new method for creating CT images from standard MRIs utilizing a transformer-based convolutional neural network (CNN) has been designed and verified by us. Synthetic CT (sCT) pictures of excellent quality and robustness may be produced in a few minutes using this model, which successfully depicts the intricate interaction among CT and MRI images. By cutting down on patient time spent on treatment planning, improving accuracy of treatment delivery, and doing away with the necessity for extra CT scans, this method may considerably streamline radiation treatment planning. © 2024 IEEE.

关键词： Radiotherapy

来源：评论

学校读者我要写书评

暂无评论

Healthcare Cost Patterns and Prediction: Investigating Personal datasets Using data Analytics 3rd

Healthcare Cost Patterns and Prediction: Investigating Perso...

引用

3rd International Conference on Signal and data Processing, ICSDP 2023

作者： Islam, Md. Aminul Nag, Anindya Chandra, Pretam Mishra, Bhupesh Kumar Fahim, S. M. Firoz Ahmed Hoque, Md Mozammel School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom College of Engineering and Physical Sciences University of Birmingham Birmingham United Kingdom Computer Science & Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science & Engineering Adamas University Kolkata700126 India Centre of Excellence for Data Science AI and Modelling University of Hull Hull United Kingdom Department of Computer Science & Engineering American International University Dhaka Bangladesh Gannon University Erie United States

ISBN: (纸本)9789819795772

The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter, since the significance of health insurance as a research topic has markedly escalated following the occurrence of the pandemic. The dataset employed in this research comprises 1338 observations, 7 columns, and corresponds to individual medical expenditures in the United States which is available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers utilized machine learning models including neural network, XAI, auto modeling to determine the correlation between pricing and the aforementioned attributes. The training process involved partitioning the dataset into a 80–20 ratio for training and evaluation respectively. As a consequence, the system achieved an accuracy rate of 97% by Gradient Boosting but we corrected it as 92% by Gradient Boosting Regressor by encoding and hyper tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Health insurance

来源：评论

学校读者我要写书评

暂无评论

Negation Blindness in Large Language Models: Unveiling the ‘NO Syndrome’ in Image Generation

arXiv

引用

arXiv 2024年

作者： Nadeem, Mohammad Sohail, Shahab Saquib Cambria, Erik Schuller, Björn W. Hussain, Amir Department of Computer Science Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Computing and Data Science Nanyang Technological University Singapore Technical University of Munich Germany Imperial College London United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Scotland United Kingdom

Foundational Large Language Models (LLMs) have changed the way we perceive technology. They have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. With the incorporation of image generation capability, they have become more comprehensive and versatile AI tools. At the same time, researchers are striving to identify the limitations of these tools to improve them further. Currently identified flaws include hallucination, biases, and bypassing restricted commands to generate harmful content. In the present work, we have identified a fundamental limitation related to the image generation ability of LLMs, and termed it "The NO Syndrome". This negation blindness refers to LLMs inability to correctly comprehend ‘NO’ related natural language prompts to generate the desired images. Interestingly, all tested LLMs including GPT-4, Gemini, and Copilot were found to be suffering from this syndrome. To demonstrate the generalization of this limitation, we carried out simulation experiments and conducted entropy-based and benchmark statistical analysis tests on various LLMs in multiple languages, including English, Hindi, and French. We conclude that the NO syndrome is a significant flaw in current LLMs that needs to be addressed. A related finding of this study showed a consistent discrepancy between image and textual responses as a result of this NO syndrome. We posit that the introduction of a ‘negation context-aware’ reinforcement learning based feedback loop between the LLM’s textual response and generated image could help ensure the generated text is based on both the LLM’s correct contextual understanding of the negation query and the generated visual output. © 2024, CC BY-NC-SA.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Emotion Classification Through EEG Signals: The EmoSense Model

Emotion Classification Through EEG Signals: The EmoSense Mod...

引用

computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yi Ken Chuah Chean Khim Toa Sim Kuan Goh Chow-Khuen Chan Hiroki Takada School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Malaysia Department of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

In the Emotion research, there has been extensive work on emotion classification using electroencephalogram (EEG) signals. Despite most studies having achieved high accuracy, there is a potential for improvement in the model, particularly in training time reduction to optimize the modeling efficiency. Hence, our study aims to develop an efficient EEG-based emotion classification model that achieves high accuracy with a reduced number of training epochs. The proposed model, named EmoSense, employs a hybrid deep learning neural network architecture, which comprises a combination of Convolutional Neural Network (CNN), Deep Autoencoder (DAE), Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism. EEG features are first extracted using Differential Entropy (DE) on the EEG channels, which are then structured to form a four-dimensional (4D) representation of features with temporal, spatial, and frequency information. Subsequently, the extracted features will be used as input to the proposed model for training. Experimental evaluation of the publicly available DEAP dataset demonstrated that the proposed model achieved an accuracy of 94.11% with only 35 epochs. This performance is comparable with the result of the Cascaded Convolutional Recurrent Neural Networks model developed by Meng et al., which reported an accuracy of 94.64% with 100 epochs. The primary reason for the comparison with Meng et al. is that our overall process, including feature extraction and formation, is similar to their study, with the only difference being the deep learning architecture. Our finding indicates that the EmoSense model is not only able to achieve comparable accuracy but also reduces the number of training epochs, demonstrating the potential to learn the EEG features in a shorter time and lowering the computational cost. This study will benefit EEG-based emotion recognition to be more accessible and practical for real-world applications.

关键词： Training Emotion recognition Accuracy Attention mechanisms Computational modeling Brain modeling Feature extraction Electroencephalography Convolutional neural networks Tuning

来源：评论

学校读者我要写书评

暂无评论

Analysis and Detection of Fake News Using Machine Learning 3

Analysis and Detection of Fake News Using Machine Learning

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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：